Presentation

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Chapter One

MAINTENANCE

1.1 INTRODUCTION

1.1.1 THE CHANGING WORLD OF MAINTENANCE

Over the past thirty years, maintenance has changed, perhaps more so than any

other management discipline. The changes are due to a huge increase in the

number and variety of physical assets (plant, equipment and buildings) which

must be maintained throughout the world, much more complex designs, new

maintenance techniques and changing views on maintenance organization and

responsibilities.

Maintenance is also responding to changing expectations. These include a

rapidly growing awareness of the extent to which equipment failure affects

safety and the environment, a growing awareness of the connection between

maintenance and product quality, and increasing pressure to achieve high plant

availability and to reduce costs.

The changes are testing attitudes and skills in all branches of industry to the

limit. Maintenance people have to adopt completely new ways of thinking,

planning and acting, as engineers and as managers. At the same time the

limitations of maintenance systems are becoming increasingly apparent, no

matter how much they are computerized.

In the face of this avalanche of change, managers everywhere are looking for a

approach to maintenance. They want to avoid the false starts and dead ends

which always accompany major upheavals. Instead they seek a strategic

framework which synthesizes the new developments into a coherent pattern, so

that they can evaluate them sensibly and apply those likely to be of most value

to them and their companies.

1.1.2 THE FIRST GENERATION

The First Generation covers the period up to World War II. In those days

industry was not very highly mechanized, so downtime did not matter much.

This meant that the prevention equipment failure was not a very high priority in

the minds of most managers. At the same time, most equipment was simple and

much of it was over-designed. This made it reliable and easy to repair. As a

result, there was no need for systematic maintenance of any sort beyond simple

cleaning and lubrication routines. The need for skills was also lower than it is

today.

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1.1.3 THE SECOND GENERATION

Things changed dramatically during World War II. Wartime pressures increased

the demand for goods of all kinds while the supply of industrial manpower

dropped sharply. This led to increased mechanization. By the 1950‟s machines

of all types was more numerous and more complex. Industry was beginning to

depend on them.

As this dependence grew, downtime came into sharper focus. This led to the

idea that equipment failures could and should be prevented, which led in turn to

the concept of preventive maintenance. In the 1960‟s, this consisted mainly of

equipment overhauls done at fixed intervals.

The cost of maintenance also started to rise sharply relative to other operating

costs. These led to the growth of maintenance planning and control, and are

now an established part of the practice of maintenance.

Finally, the amount of capital tied up in fixed assets together with a sharp

increase in the cost of that capital led people to start seeking ways in which they

could maximize the life of the assets.

1.1.4 THE THIRD GENERATION

Since the mid-seventies, the process of change in industry has gathered even

greater momentum. The changes can be classified under the headings of new

expectations, new research and new techniques.

1.1.5 MAINTENANCE

From the engineering viewpoint, there are two elements to the management of

any physical asset. It must be maintained and from time to time it may also need

to be modified.

The major dictionaries define maintain as cause to continue (Oxford) or keep in

an existing state (Webster). This suggests that

Maintenance means preserving something. On the other hand, they agree that to

modify something means to change it in some way.

When we set out to maintain something, what is it that we wish to cause to

continue? What is the existing state that we wish to preserve?

The answer to these questions can be found in the fact that every physical asset

is put into service because someone wants it to do something. In other words,

they

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expect it to fulfill a specific function or functions. So it follows that when we

maintain an asset, the state we wish to preserve must be one in which it

continues to do whatever its users want it to do.

Maintenance: Ensuring that physical assets continue to do what their users

want them to do.

1.2 FUNCTIONS AND PERFORMANCE STANDARDS

Before it is possible to apply a process used to determine what must be done

to ensure that any physical asset continues to do whatever its users want it

to do in its present operating context, we need to do two things:

determine what its users want it to do

ensure that it is capable of doing what it users want to start with.

Primary functions, which summarizes‟ why the asset was acquired in the

first place. This category of functions covers issues such as speed, out-put

carrying or storage capacity, and product quality and customer service.

Secondary functions, which recognize that every asset is expected to do

more than simply fulfill its primary functions. Users also have

expectations in areas such as safety, control, containments, comfort,

structural integrity, economy, protection, efficiency of operation,

compliance with environmental regulations and even the appearance of

the asset.

1.3 OBJECTIVES OF MAINTENANCE

The objectives of maintenance are defined by the functions and associated

performance expectations of the asset under consideration. But how does

maintenance achieve these objectives?

The only occurrence which is likely to stop any asset performing to the standard

required by its users is some kind of failure. This suggests that maintenance

achieves its objectives by adopting a suitable approach to the management of

failure.

However,

firstly, by identifying what circumstances amount to a failed state

then by asking what events can cause the asset to get into a failed state.

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In the world of RCM, failed states are known as functional failures because

they occur when an asset is unable to fulfill a function to a standard of

performance which is acceptable to the user.

1.4 FAILURE MODES

As mentioned all the events which are reasonably likely to cause each failed

state. These events are known as failure modes „Reasonably likely‟ failure

modes include those which have occurred on the same or similar equipment

operating in the same context, failures which are currently being prevented by

existing maintenance regimes, and failures which have not happened yet but

which are considered to be real possibilities in the context in question.

Most traditional lists of failure modes incorporate failures caused by

deterioration or normal wear and tear.

1.5 FAILURE EFFECTS

Failure effects, which describe what happens when each failure mode occurs.

These descriptions include all the information needed to support the evaluation

of the consequences of the failure, such as:

what evidence (if any) that the failure has occurred

in what ways (if any) it poses a threat to safety or the environment

in what ways (if any) it affects production or operations

what physical damage (if any) is caused by the failure

what must be done to repair the failure.

The process of identifying functions, functional failure modes and failure effects

yields surprising and often very exciting opportunities for improving

performance and safety, and also for eliminating waste.

1.6 FAILURE CONSEQUENCES

Failure consequences can be classified into four groups:

Hidden failure consequences: Hidden failures have no direct impact, but

they expose the organization to multiple failures with serious, often

catastrophic, consequences. (Most of these failures are associated with

protective devices which are not fail-safe.

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SAFETY AND ENVIRONMENTAL CONSEQUENCES: A failure

has safety consequences if it could hurt or kill someone. It has

environmental consequences if it could lead to a breach of any corporate,

regional national or international environmental standard.

Operational consequences: A failure has operational consequences if it

affects production (output, product quality, customer service or operating

costs in addition to the direct cost of repair)

Non-operational consequences: Evident failures which fall into this

category affect neither safety nor production, so they involve only the

direct cost of repair.

The consequence evaluation process shifts emphasis away from the idea that all

failures are bad and must be prevented. In so doing helps us to, it focuses

attention on the maintenance activities which have most effect on the

performance of the organization, and diverts energy away from those which

have little or no effect. It also encourages us to think more broadly about

different ways of managing failure.

1.7 Age and Deterioration

Any physical asset which is required to fulfill a function which brings it into

contact with the real world will be subjected to a variety of stresses. These

stresses cause the asset to deteriorate by lowering its resistance to stress.

Eventually this resistance drops to the point at which the asset can no longer

deliver the desired performance – in other words, it fails.

Exposure to stress is measured in a variety of ways including output, distance

travelled, operating cycles, calendar time or running time. These units are all

related to time, so it is common to refer to total exposure to stress as the age of

the item. This connection between stress and time suggests that there should be

a direct relationship between the rate of deterioration and the age of the item. If

this is so, then it follows that the point at which failure occurs should also

depend on the age of the item.

Deterioration is directly proportional to the applied stress, and

The stress is applied consistently.

If this were true of all assets, we would be able to predict equipment life with

great precision. The classical view of preventive maintenance suggests that this

can be done – all we need is enough information about failures.

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Most people still tend to assume that similar items performing a similar duty

will perform reliably for a period, perhaps with a small number of random early

failures, and then most of the items will „wear out‟ at about the same time.

Age-related failure patterns apply to items which are very simple, or to complex

items which suffer from a dominant failure mode. In practice, they are

commonly found under conditions of direct wear (most often where the

equipment comes into direct contact with the product). They are also associated

with fatigue, corrosion, oxidation and evaporation.

Examples of points where equipment comes with the product include furnace

refractory, pump impellers, valve seats, machine tooling, screw conveyors,

crushers and hopper liners, the inner surfaces of pipelines, dies and so on.

Fatigue affects items- especially metallic items- which are subjected to

reasonably- high frequency cyclic loads. The rate and extent to which oxidation

and corrosion affect any item depend of course on its chemical composition, the

extent to which it is protected and the environment in which it is operating.

Evaporation affects solvents and lighter fractions of petrochemical products.

Two preventive options which are available under these circumstances are

scheduled restoration task and scheduled discard tasks.

Scheduled Restoration Task

As the name implies, scheduled restoration entails taking periodic action to

restore an existing item or component to its original condition ( or more

accurately, to restore its original resistance to failure). Specifically:

‘Scheduled restoration entails remanufacturing

a single component or overhauling an entire

assembly at or before a specified age limit,

regardless of its condition at the time’.

Scheduled tasks are also known as scheduled rework tasks. As the above

definition suggest, they include overhauls which are done at pre-set intervals.

Examples: locomotives, wagons , coaches, signaling point, e.t.c.

The Frequency of Scheduled Restoration Tasks

The frequency of a scheduled restoration task is governed

by age at which the item or component shows a rapid

increase in the conditional probability of failure

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In practice, the frequency of a scheduled restoration task can only be determined

satisfactorily on the basis of reliable historical data. This is seldom available

when assets first go into service, so it is usually impossible to specify scheduled

restoration tasks in prior-to-service maintenance programs. However, items

subject to very expensive failure modes should be putting age exploration

programs as soon as possible to find out if they would benefit from scheduled

tasks.

1.8 The Technical feasibility of Scheduled Restoration

The above comments indicate that for a scheduled restoration task to be

technically feasible, the first criteria which must be satisfied are that

There must be a point at which there is an increase in the conditional

probability of failure ( in other words, the item must have a „life‟)

We must be reasonably sure what the life is.

Secondly, most of the items must survive to this age. If too many items fail

before reaching it, the net result is an increase in unanticipated failures. Not

only could this have unacceptable consequences, all the items must survive to

the age at which the scheduled restoration task is to be done, because we cannot

risk failures which might hurt people or damage the environment.

Finally, scheduled restoration must restore the original resistance to failure of

the asset, or at least something close enough to the original condition to ensure

that the item continues to be able to fulfill its intended function for a reasonable

period of time.

Scheduled restoration task are technically feasible if:

There is an identified age at which the item shows a rapid increase in

the conditional probability of failure.

Most of the items survive to that age( all of the items if the failure has

safety or environmental consequences)

They restore the original resistance to failure of the item.

1.9 The Effectiveness of Scheduled Restoration Task

Even if it is technically feasible, scheduled restoration might still not be worth

doing because other tasks may be even more effective.

If a more effective task cannot be found, there is often a temptation to select

scheduled restoration tasks purely on the grounds of technical feasibility. An

age limit applied to an item means that some

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items will receive attention before they need it while others might fail early, but

the net effect may be an overall reduction in number of unanticipated failures.

However even then scheduled restoration might not be worth doing, for the

following reasons.

As mentioned earlier, a reduction in the number of failures is not

sufficient if the failure has safety or environmental consequences,

because we want to eliminate these failure altogether.

If the consequences are economic, we need to be sure that over a period

of time, the cost of doing the scheduled restoration task is less than the

cost of allowing the failure to occur. When comparing the two, bear in

mind that an age limit lowers the service life of any item, so it increases

the number of items sent to the workshop for restoration.

When considering failures which have operational consequences, bear in mind

that a scheduled restoration task may affect operations. In most cases, this effect

is likely to be less than the consequences of the failure because:

The scheduled restoration task would normally be done at a time when it

is likely to have the least effect on production (usually during a so called

production window).

The scheduled restoration task is likely to take less time than it would to

repair the failure because it is possible to plan more thoroughly for the

scheduled task.

If there are no operational consequences, scheduled restoration is only justified

if it costs substantially less than the cost of repair (which may be the case if the

failure causes extensive secondary damage).

1.10 Scheduled Discard Tasks

Again as the name implies, scheduled discard means replacing an item or

component with a new one at pre-set intervals. Specifically:

Reliability - Centered Maintenance

Scheduled discard entails discarding an item

or component at or before a specified age limit,

regardless of its condition at the time

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These tasks are done on the understanding the replacing the old component with

a new one will restore the original resistance to failure.

1.11 The Frequency of Scheduled Discard Tasks

Like scheduled restoration tasks are only technically feasible if there is a direct

relationship between failure and operating age. The frequency at which they are

done is determined on the same basis, so:

The frequency of a scheduled discard task is governed by

the age at which the item or component shows a rapid

increase in the conditional probability of failure.

In general, there is a particularly widely held belief that all items‟ have a life‟

and that installing a new part before this‟ life‟ is reached will automatically

make it‟ safe‟. This is not always true, so RCM takes special care to focus on

safety when considering scheduled discard tasks.

For this reason, there are two different types of life-limits when dealing with

scheduled discard tasks. The first apply to tasks meant to avoid failures which

have safety consequences, and are called safe-life limits. Those which are

intended to prevent failures which do not have safety consequences are called

economic-life limits.

1.12 Safe-life limits

Safe-life limits only apply to failures which have safety or environmental

consequences so the associated tasks must prevent all failures for example

signaling apparatus communications. In other words, no failures should occur

before this limit is reached.

In practice, safe-life limits can only apply to failure modes which occur in such

a way that no failures can be expected to occur before the wear out zone is

reached.

Ideally, safe-life limits should be determined before the item is put into service.

It should be tested in a simulated operating environment to determine what life

is actually achieved, and a convective fraction of this life used as the safe-life

limit.

There is never a perfect correlation between a test environment and the

operating environment. Testing a long-lived part to failure is also costly and

obviously takes a long time, so there is usually not enough test data for survival

curves to be drawn with confidence. In these cases safe-life limits can be

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established by dividing the average by an arbitrary factor as large as three and

four. This implies that the conditional probability of failure at the safe-life limit

would essentially be zero. In other words the safe-life limit is based on 100%

probability of survival to that age.

The function of a safe-life limit is to avoid the occurrence of a critical failure, so

the resulting discard task is worth doing if it ensures that no failure occur before

the safe-life limit.

1.13 Economic-life limits

Operating experience sometimes suggests that the scheduled discard of an item

is desirable on economic grounds. This is known as an economic-life limit. It is

based on the actual age-reliability relationship of the item, rather than a fraction

of the average age at failure.

The only justification for an economic life limits are cost-effectiveness. In the

same way that scheduled restoration increases the number of jobs passing

through the workshop, so scheduled discard. As a result, the cost-effectiveness

of scheduled discard tasks is determined in the same way as it is for scheduled

restoration tasks.

In general, an economic life-limit is worth applying if it avoids or reduces the

operational consequences of an unanticipated failure, or if the failure which it

prevents causes significant secondary damage. Clearly, we must know the

failure pattern before we can assess the cost effectiveness of scheduled discard

tasks.

1.14 The Technical Feasibility of Scheduled Discard Tasks

Scheduled discard tasks are technically feasible under the following

circumstances:

Scheduled discard tasks are technically feasible if:

There is an identifiable age at which the item shows a rapid increase in

the conditional probability of failure

Most of the items survive to that age (all of the items if the failure has

safety or environmental consequences).

1.15 Failures which are not Age-related

This is due primarily to a combination of variation in applied stress and

increasing complexity.

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Many failures are caused by increases in applied stress, which are caused in turn

by incorrect operation, incorrect assembly or external damage. (Ideally,

„preventing‟ failures of this sort should be a matter of preventing whatever

causes the increase in stress levels, rather than a matter of doing anything to the

asset.)

Items are made more complex to improve their performance (by incorporating

new or additional technology or by automation) or to make them safer (using

protective devices).

In other words, better performance and greater safety are achieved at the cost of

greater complexity means balancing, with the size and mass needed for

durability. This combination of complexity and compromise:

Increase the number of components which can fail, and also increases the

number of interfaces or connections between components. This in turn

increases the number and variety of failures which can occur.

Reduces the margin between the initial capability of each component and

the desired performance (in other words, the „can‟ is closer to the „want‟),

which reduces scope for deterioration before failure occurs.

These two developments in turn suggest that complex items are more

likely to suffer from random failures than simple items.

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2.0 INTRODUCTION

The introduction of highly reliable sensors, remote condition monitoring

equipment, data acquisition, data analysis, e.t.c will change the form and

functionality of engineering systems and maintenance within any infrastructure

sectors. Infrastructural companies use intelligent today to increase reliability,

safety and reduce cost. It is vital to know that this intelligent infrastructure will

create human factor challenges. In this paper, basic principle of intelligent

infrastructure that cut across sectors (Cloud Computing, Cognitive Computing,

Cense (sensors) Photonic etc) and human factors are discussed.

2.1 CLOUD COMPUTING

“The cloud” is simply a business model for the creation and delivery of

computer resources. The model‟s reliance on shared resources and

virtualization allows cloud users to achieve levels of economy and scalability

that would be difficult in a traditional data center. As such, the cloud is already

transforming how we access and use technology – similar to how adoption of

mass production transformed manufacturing during the Industrial Revolution.

At the same time, enterprises have been cautions about moving their workloads

to cloud services. According to a recent Frost & Sullivan survey, just 9 percent

of enterprises are currently using cloud infrastructure services. Adopters and

non-adopters alike cite concerns about security, loss of control, application

performance, and availability and resilience of workloads (e.g. storage,

corporate applications, test and developments).

So, is the cloud a friend or foe to overtaxed IT departments? The answer

depends heavily on which cloud is chosen. Although the industry refers to

“THE cloud,” it is a misnomer that can cause confusion. In fact, each cloud is

different, with each provider offering unique cloud services and configurations.

Common cloud options include:

Public cloud, in which multiple companies share physical servers and

networking resources hosted in a provider‟s data center.

Private cloud, in which companies do not share resources (although

efficiencies may be realized by hosting multiple virtual applications from

the same company on a single physical server). Private clouds can be

located either in a provider‟s data center or in the company‟s own on-

premises data center.

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Hybrid cloud, In which virtualized applications can be moved among

private and public cloud environments.

For each workload (e.g., storage, corporate applications, test and

development), enterprise IT departments must not only weigh the benefits

and risks of each option from various providers, but also weigh the value

against traditional in-house data center and hosting options.

Scalable, on-demand resources: The ability to launch a cloud

application in minutes, without having to purchase and configure

hardware, enables enterprises to significantly cut their time to market. By

taking advantage of cloud options for “bursting” during peak work

periods, enterprises can also cost-effectively improve application

performances and availability.

Budget-friendly: Cloud computing services require no capital

investment, instead tapping into the operating budget. As many

companies tighten up their processes for approval of capital expenditures,

a service can be easier and faster to approve and deploy.

Utility pricing: The pay-per-use model that characterizes most cloud

services appeals to enterprises that want to avoid overinvesting. It also

can shorten the time to recoup the investment.

Cloud computing exhibits the following key characteristics:

Cost

Agility

Virtualization

Maintenance

Security

Reliability

Device and location independence

Application programming interface

Multitenancy

Scalability and elasticity

Performance

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BENEFITS OF CLOUD COMPUTING

Achieve economies of scale – increase volume output or productivity

with fewer people.

Reduce spending on technology infrastructure – maintain easy access to

your information with minimal upfront spending. Pay as you go (weekly,

quarterly or yearly) based on demand.

Globalize your workforce on the cheap. People worldwide can access the

cloud, provided they have an internet connection.

Streamline processes. Get more work done in less time with less people.

Reduce capital costs. There is no need to spend by money on hardware,

software or licensing fees.

Improve accessibility. You have access anytime, anywhere, making your

life so much easier.

Monitor projects more effectively – stay within budget and ahead of

completion cycle times.

Less personnel training is needed – it takes fewer people to do more work

on a cloud, with a minimal learning curve on hardware and software uses.

Minimize licensing new software - stretch and grow without the need to

buy expensive software license a program.

Improve flexibility – you can change direction without serious people or

financial issue at stake.

Almost unlimited storage.

Backup and Recovery.

Automatic software integration – In the cloud, software integration is

usually something that occurs automatically. This means that you do not

need to take additional efforts to customize and integrate your

applications as per your preferences. This aspect usually takes care of

itself.

Not only that, cloud computing allows you to itemize your options with

great ease. Hence, you can handpick just those service and software

applications that you think will best suit your particular enterprise

Ease access to information

Quick development

DISADVANTAGES

1. Technical issues:

- Connecting

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- Outages

- Internet problem

- Technical issues

2. Security in the cloud:

- Surrounding company‟s sensitive information to a third party cloud

service provider.

3. Prone to attack

- Internal hack attack

Three Categories of cloud computing

1. Infrastructure as a service serves a data storage (Iass)

2. Platform as a service (Paas)

3. Software provides as a service (Saas) with access to already created

applications that are operating in the cloud.

Infrastructure as a service (IaaS)

1. In the most basic cloud-service model, providers of IaaS offer computer –

physical or (more often) virtual machines – and other resources. IaaS

clouds often offer additional resources such as images in a virtual-

machine image-library, raw (block) and file-based storage, firewalls, load

balancers. IP address, virtual local area networks (VLANs), and software

bundles.

To deploy IaaS applications, cloud users install operating-system images

and their application software on the cloud infrastructure.

2. Software as a service (SaaS):

Provides use with access to already created applications that are operating

in the cloud.

Cloud providers install and operate application software in the cloud and

cloud users access the software from cloud provider. The clients do not

manage the cloud infrastructure and platform on which the application is

running. This removes the need to install and run the application on the

cloud user‟s own computers simplifying maintenance and support.

3. Platform as a service (PaaS)

Cloud providers deliver computing platform typically including operating

system, programming language execution environment, database, and

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web server. Application developer can develop and run their software

solutions on a cloud platform without the cost and

complexity of buying and managing the underlying hardware and

software layers. With some PaaS offers, the underlying computer and

storage resources scale automatically to match application demand such

that cloud user does not have to allocate resources manually.

2.2 COGNITIVE COMPUTING

Cognitive Computing refers to the development of computer systems modeled

after the human brain. Originally referred to as an artificial intelligence,

researchers began to use the term cognitive computing instead in the 1990‟s to

indicate that the science was designed to teach computers to think like a human

mind, rather than developing an artificial system. Cognitive Computing

integrates technology and biology in an attempt to re-engineer the brain as one

of the most efficient and effective computer on earth.

Cognitive Computing has its roots in the 1990‟s when computer companies first

began to develop intelligent computer systems. Most of their systems were

limited, however because they could not learn from their experience. Early

artificial intelligence could be taught a set of parameters, but was not capable of

making decisions for itself or intelligently analyzing a situation and coming up

with a solution. Enthusiasm for the technology began to wane, as Scientist

feared that an intelligent computer could never be developed.

However, with major advanced in cognitive science, researchers interested in

computer intelligence became enthused. Deeper biological understanding of

how the brain worked allowed scientists to build computer systems modeled

after their mind and most importantly to build a computer that could integrate

past experience into its systems. Cognitive Computing was reborn with

researchers at the turn of the 21st century developing computers which operated

at the higher rate of speed that the human brain did.

Cognitive computing integrates the idea of a neural network, a series of events

and experiments which the computer organizes to make decisions; the neural

network contributes to the compiles body of knowledge about a situation and

allows it to make an informed choice and proficiently to work around an

obstacle or a problem.

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Cognitive Computing researchers argue that the brain is a type of machine and

can therefore potentially be replicated; the development of neural network was a

large step in this direction.

As the body of knowledge about the brain grows and scientist experiment more

with cognitive computing, intelligent computers are the result from computers

which are capable of recognizing voice commands and acting from them for

example, are used in many navigation systems on board aircraft and boats and

while these systems often cannot handle crises they can operate the craft under

normal conditions.

At the turn of the 21st century, many researchers believed that cognitive

computing was the hope of near future. By replicating the human brain in

computer form, researchers hope to improve conditions for the human as well as

gaining a deeper understanding of the biological reactions that power the brain.

Computers capable of reason were begin to emerge in the late 1990s with hopes

for consciousness following.

Hear and See with the aid of camera but computers should be able to interpret

images more intuitively from telling whether a picture is on a beech or in a send

box to whether a mole should be examined by a doctor. It‟s also whether will

let our cars and robots operate safely.

Sound Chatting on line and dictation. But by listening closely and adding

context to sounds in the environment a computer may be able to tell you

whether your baby‟s crying means distress, hunger or just a need of attention.

And later some patterns could be detected and shared among a network of

computers to highly predict disasters and weather patterns

Touch:- means more that a touch screen. Your device can feel your finger but

what do you feel? A glass or plastic screen. Researchers are working on

creating tailored vibration that could let you feel textures instead, from clothing

materials to someone else skin.

Smell:- Subtle chemical signals that we take for granted – smoke (locomotive

engine), perfume, wet dog are powerful clues to what is happening in our

surroundings. We all have simple smell sensors, smoke and carbon monoxide

detectors in our homes. But more sophisticated sensor could detect alcohol on

someone (Breath from Locomotive drivers) in a loco, sense early signs of

infections or disease in our driver (to prevent accident) or just let you know the

viscosity of the oil.

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Taste:- Computer design a school lunch for family dinner that has been adjusted

for the dietary needs and restrictions from each individual. Obesity from

diabetes, this will be helpful in our hospitals and clinics.

Chemical Sensors in your phones that sense your dinner and suggest a pairing

wire (arrangement of wagon with loads.

2.3 CeNSE

CeNSE or the nervous system of the Earth, consisting of a trillion nanoscale

sensors and activators embedded in the environment and connected via an array

of networks with computing systems, software and service to exchange their

information among analysis engines, storage systems and end users.

2.3.1 SENSORS

A sensor (also called detector) is a converter that measures a physical quantity

and converts it into signal which can be read by an observer or by and

instrument (mostly electronic today) e.g. mercury in glass thermometer,

thermocouple converts temperature into an output voltage which can be read by

voltmeter.

A sensor is a device which receives and responds to a signal when touched.

Sensors sensitivity indicates how much the sensor‟s output changes when the

measured quantity changes. Sensors that measure very small changes must

have very high sensitivities. Sensors have an impact on what they measure.

Sensors need to be designed to have a small or little effect on what is measured;

e.g. if its mercury in a thermometer moves 1cm when the mercury temperature

changes by 1oC the sensitivity is 1cm/

oC (it is basically the slope dy/dx

assuming a linear characteristics)

2.3.2 CLASSIFICATION OF SENSORS MEASUREMENT ERRORS

A good sensor obey the following rules

(a) Is sensitive to measured property only

(b) Is insensitive to any other property likely to be encountered in its

application.

(c) Does not influence the measured property.

Sensitivity of a sensor is defined as the ratio between output signal and a

measured property.

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2.3.3 SENSOR DEVIATION

If the sensor is not ideal, several types of deviations can be observed

(a) The sensitivity in practice differs from the value specified (sensitivity

error though the sensor is linear).

(b) Since the range of the output signal is always limited, the output signal

will eventually reach a minimum or maximum when the measured

property exceeds the limits. The full scale range defines the maximum

and minimum values of the measured property.

(c) If the output signal is not zero (0) when the measured property is zero, the

sensor has an offset of bias. This is defined as the output of the sensor at

zero input.

(d) If the sensitivity is not constant over the range of the sensor, this is called

non-linearity. The sensor is called non-linearity sensor.

(e) If the deviation is caused by a rapid change of the measured property

overtime, there is a dynamic error. This error is known as bode plot

showing sensitivity error and phase shift as function of a frequency of a

periodic input signal.

(f) If the output signal slowly changes independent of the measured property,

this is defined as a drift.

(g) Noise is a random deviation of the signal that varies in time.

(h) Hysteresis is an error caused by when the measured property reverses

direction but there is some finite lap in time for the sensor to respond,

creating a different offset error in one direction than in the other.

(i) Digitalization Error – if the sensor has a digital output, the output is

essentially an approximation of the measured property

2.3.4 WAYS OF MINIMIZING THE SYSTEMATIC ERRORS OR RANDOM ERRORS

(1) Calibration Strategy

(2) Noise can be reduced by signal processing such as filtering.

2.3.5 TYPES OF SENSORS

(1) Biosensors – In biomedicine and biotechnology sensors which detect

analytes, in biological components such as cells, protein, nucleic acid or

biometric polymers are called biosensors.

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(2) Nanosensors – these are non-biological sensors even organic for

biological analytes is referred to as a sensor or nanosensor e.g. micro

cantilevers.

2.3.6 WIRELESS SENSOR NETWORK

A wireless sensor network (WSN) consists of spatially distributed autonomous

sensors to monitor physical or environmental conditions, such as temperature,

sound, pressure etc and to cooperatively pass their data through the network to a

men location. The more modern networks are bi-directional, also enabling

control of sensors activity. It can be used for surveillance, industrial and

consumer applications, such as industrial process monitoring and control,

machine health monitoring etc.

The Wireless Sensor Network is build of nodes which can be few hundreds,

thousands where each node is connected to one or sometimes several sensors.

Each sensor network has typically several parts.

(1) Radio transceiver with an internal antenna or external antenna.

(2) Microcontroller, an electronic circuit for interfacing with sensors.

(3) Energy source, usually a battery or an embedded form of energy

harvesting.

(4) Typology of the Wireless Sensor Network vary from a simple star

network to an advanced multi-hop wireless mesh network.

(5) The propagation technique between the hops of the network can be

routing or flooding.

2.3.7 APPLICATION

(1) Area monitoring – In area monitoring, the Wireless Sensor Network is

deployed over a region where some phenomenon is to be monitored e.g.

geo-fencing of gas or oil pipeline.

(2) Environmental/Earth monitoring

(i) Sensing Volcanoes (Wash out) etc

(ii) Oceans, glaciers, forests etc

(3) Air quality monitoring (Loco), Printing press.

The degree of pollution in the air has to be measured frequently in order

to safeguard people and the environment from any kind of damages due

to pollution e.g Gas

(4) Interior Exterior Monitoring

(5) Forest fire detector to protect our cables (Telephone etc).

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(6) Landslide detection System (Civil) makes use of a wireless sensor

network to detect the slight movements of soil and changes in various

parameters that may occur before or during a landslide through the data

gathered, it may be possible to know the occurrence of landslide before it

actually happens.

(7) Water Quality Monitoring- The use of many wireless distributed sensors

enable the creation of a more accurate map of the water status, and allows

the permanent deployment of difficult access without the need of manual

data retrieval.

(8) Natural Disaster Prevention e.g. floods – wireless nodes have

successfully been deployed in rivers where changes of the water levels

have to be monitored in real time.

(9) Industrial Monitoring – machine health monitoring – wireless sensor

networks have been developed for machinery condition based

maintenance (CBM) as they offer significant cost savings and enable new

functionalities. In wired systems, the installation of enough sensors is

often limited by the cost of wiring. Previously inaccessible locations,

rotating machinery, hazardous or restricted areas and in mobile assets can

now be reached with wireless sensors.

(10) Data Logging

2.3.8 CHARACTERISTICS OF WIRELESS SENSOR NETWORK

(1) Power consumption constraints for nodes using batteries or energy

harvesting

(2) Ability to cope with node failures

(3) Mobility of nodes

(4) Communication failures

(5) Heterogeneity of nodes

(6) Scalability to large scale of deployment

(7) Stability to withstand harsh environmental conditions.

(8) Ease of use.

2.4 DATA ACQUISITION

Data acquisition is the process of sampling signals that measures world physical

conditions and converting the resulting samples into digital numeric values that

can be manipulated by a computer.

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It converts the analog waveforms into digital values for processing components

of DAQ or DAS are:

(1) Sensors

(2) Signal conditioning circuitry to convert sensors signals into a form that

can be converted to digital values.

(3) Analog to digital converters, which convert conditioned sensor signals to

digital values.

(4) Software programs using java, LISP, Pascal etc.

(5) DAQ Hardware – interfaces between the signal and a PC. It could be

inform of a modular that can be connected to the computer‟s port

(parallel, serial, USB etc or cards connected to slots (MCS).

INPUT DEVICES

- Analog to digital converter

- Time to digital converter

HARDWARES

CAMAC – Computer Automated Measurement and Control

- Industrial Control

- Industrial USB

- LAN extensions for Instrumentations

- NIM

- Power Lab

- PC1 extensions for Instrumentation

Graphical programming environments include ladder, logic, visual CH, Visual

Basic and Lab view.

2.5 HUMAN FACTOR

2.5.1 GENERAL PROBLEM IN COMMUNICATION

While there are many forms of communication that take place within a railway

system the work reviewed here is limited to communication between drivers,

signalers and trackside workers. Potential problems and misunderstandings in

communication can arise when two people who are separated by location

(driver/signaler) are trying to talk to each other. The problem generally revolves

around a misunderstanding of the intend meaning of the communication.

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Gibson [1] reviewed the literature on communication in general although

incorrectly assumed there was no previous work directly relevant to the rail

industry (see report by Arthur D. Little Ltd. [2]. However, there is a general

need to investigate a wide variety of communication processes within the rail

industry. Gibson identified three sources of communication failure, associated

with the sender, language used, and hardware. Only the first two of these lie

clearly within the Human Factor domain, and are relevant within a number of

situations where railway personnel have to communicate with each other over

the radio or telephone. These include driver-signaler communication and

signaler – PICOP (person in Charge of Possession) or more recently signaler-

COSS (Controller of Site Safety).

2.5.2 DRIVER-SIGNALLER COMMUNICATION

Arthur D. Little Ltd. [2] was commissioned to investigate communication risk

between drivers and signalers. Unlike the drivers and signalers only

communicate with each other when the driver has been brought to a halt at a

signal failed at danger, or in an emergency. Three generic errors were identified

in the scenario where a train has been stopped at a signal at danger, all

encompassed by Gibson‟s framework: the driver mistakenly believes they have

been authorized to pass a signal at danger; the signaler correctly authorizes the

wrong train; the signaler incorrectly authorizes the correct train.

The impact of the first and the third of these errors is potentially catastrophic.

While the report by Arthur D. Little concluded that the current procedures were

sufficient to ensure safe operation at minimal risk, the potential for error (and

hence potential catastrophe) still exists. It is necessary to gain a better

understanding of the mechanism of communication between drivers and

signalers, where potential for error lies and the possible causes of deviation

from correct procedure (e.g., fatigue, distraction). New technology (e.g., in-cab

displays) will inevitably impinge upon the driver-signalers dynamic and attempt

to assess how best to integrate this technology into the rail network from a

Human Factor perspective.

2.5.3 SIGNALLER – PICOPs/COSS

PICOPs, or more recently COSS, take possession of a block of track when

maintenance work required. This requires coordination between the signaler and

COSS in order to ensure the safety of the trackside workers. Halliday explained

the procedures to ensure safety, this information needs to be structured and

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involve the use of correct radio discipline (such as using the phonetic

alphabetic) in order to minimize potential errors in communication such as those

formulated by Gibson [1]. One of the aims of the proposed work is to gain a

deeper understanding of how different functions within the rail system interact

and how these interactions are influenced by the organizational context within

which they take place. Improvement in communication between Signaller and

COSS (and hence the safety of trackside workers) may require alternative

interventions that go beyond simply adhering to radio discipline.

As mentioned above, Roth et al. [3] highlighted the advantages of shared or

“open” radio communication channels where all rail personnel can listen in and

selectively attend to relevant information. In rail network, VHF radio use “open

channels” to allow monitoring of background information keeping personnel up

to dare with what is happening across the system. Hence they can quickly and

appropriately attend to any emergencies.

2.5.4 IMPACT OF FATIGUE ON DRIVER PERFORMANCE AND SAFETY

2.5.4.1 DETECTING FATIGUE STATES IN DRIVERS

Research on fatigue, within the railways as elsewhere, fails to distinguish the

general behavioural outcome (tiredness) and the possible causes of the state. In

particular, fatigue is often ascribed to sleepiness brought about by sleep

deprivation or poor management of shift cycles, and the problem for

performance typically identified with the increased risk of eye closure or actual

sleep. It is important to recognize that mental fatigue can result entirely from

overwork, in the form sustained cognitive operations, even with normal sleep

and well-adjusted shift cycles. Hockey & Meijman [4] have identified at least

three different forms of fatigue-mental, sleep-based and physical, which have

quite different origins requiring different management solutions and

countermeasures. In this paper, some of the issues relating to sleep loss and shift

working, both of which can cause dramatic losses of attention, but largely

ignored the problem of fatigue from sustained demanding cognitive work.

These other effects are more subtle, and their effects therefore more insidious.

They affect information processing strategies by reducing the operator‟s

commitment to high effort attention states. Within rail systems research, the

notion of the train driver as an information processor (rather than someone

engaging in heavy physical work) was introduced over 30years ago [5]. Grant‟s

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suggestions for research on mental fatigue include simultaneous capture of

behavioural observations and physiological measures.

This is the approach taken in major recent programmes aimed at identifying

markers of strain as a basis for predicting performance breakdown in aviation,

and recognizes that risk is related to a progressive effect of the onset of fatigue.

However, over the intervening period since Grant‟s report, little or no work has

been conducted using this methodology. Instead the main emphasis has been on

inferring casual patterns from accident data and shift work patterns. This is still

a viable approach, but an analysis of fatigue requires much better predictors

than can be gained from overt performance measure alone. The use of failsafe

devices (such as ATP of RPWS for SPADs) is an extreme technical response to

failure of the driver‟s concentration, bringing the system to a halt and

necessitating considerable disruption, as well as reducing confidence in the

driver.

2.5.4.2 IMPACT OF SHIFT WORK ON FATIGUE

Shift work is identified as a major contributory factor to fatigue as the internal

body clock fails to adjust to shift work and leads to an accumulation of sleep

loss due to working shifts. A number of the major findings from examining shift

work patterns and their subsequent impact on fatigue are reported by Folkard &

Sutton [6].

During nightshifts one of the major findings is of reduced alertness and

performance due to the internal body clock gearing up for sleep rather than

work. In order to recover from the effects of shifts work the main consensus of

opinion is that the recovery period should allow sufficient time to recover from

accumulation of fatigue. However, while the review by Folkard and Sutton is

extensive it is in contrast with the findings of Wharf [7] who found that while

the consensus is that there is a decrement in performance during the night shift.

In conclusion, information within such a rail system will be distributed widely

across signalers, controllers, drivers and trackside workers as well as potential

in-cab information systems and train operating companies (TOCs). Information

regarding the state of the network will flow between all these agents in the

systems and will be represented not only externally (in the form of signals,

information displays etc) but also internally (in terms of the cognitive

processing of the controller/driver/signaler etc) Fig. 1 shows a simplified

representation of such a system, highlighting the flow of information.

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Figure 1: A model of the network operating environment within which drivers

(D), signalers (S), controllers (C), and maintenance and the implications of this

for the rail network.

Between the various functions/elements of the network operating environment

and how these interactions bear directly upon the effectiveness, reliability, and

safety of the broader rail environment, as well as the costs (financial, resources,

and other) of these outcomes. Adopting an approach of this kind to railway

Human Factor issues will allow a much broader understanding of the processes

taking place, within the system, as well as providing a more supportive

explanatory framework for determining the origins and solutions of the

problems of inefficiencies and error associated with Human Factor. It is based

of these inefficiencies error the intelligence infrastructure is vital to minimize or

reduce human factor.

2.6 PHOTONICS

The science of photonics includes the generation, emission, transmission,

modulation, signal processing, switching, application, as detection sensing of

light. Photonic have both wave and particle nature.

Optical and photonic computing is intertwined to use photonics or light particles

produced by lasers or diodes in place of electrons. Compared to electrons,

photonics have a higher bandwidth. Most research projects focus on replacing

current computer components with optical equivalents resulting in an optical

digital computer system process binary data. The fundamental before building

block of modern electronic computer is the transistor. To replace the electronics

components with “optical transistor” is required. This is achieved using

materials with a non-linear refractive index. In particle, materials exist where

Rail Environment

Effectiveness Reliability

Costs

Safety

Network Operating Environment

C

D

M

S

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the intensity of incoming light, the intensity of light transmitted through one

material in a single manner to the voltage response of an electronic transistor.

Such an “optical transistor can be used to create optical logic gates which in

turn are assembled into the higher level components of the computer Central

Processing Unit (CPU)

Photonic logic is the use of photons (light) on logic gates (NOT, AND, OR,

NAND, NOR XOR, YNOR). Switching is obtained using non linear optical

effects when two or more signal is combined.

2.7 NON VOLATILE MEMORY

F-RAM products combine the non –volatile data storage capability or ROM

with the benefits of RAM, which include a high number of read and write

cycles, high speed read and write cycles, and low power consumption. FRAM,

product line features various interfaces and densities which include industry

standard serial and parallel interface, industry standard package types, as

4kilobites, 16kilobites, 64kilobites, 25kilobites, 1megabite, 2megabites and

4megabit densities.

F-RAM performs read and write operation of the same speed, there are no

delays as before in non-volatile. Floating gate memories have long write delay

of 5 seconds. FRAM writes in nano seconds essential in application like auto

safety system.

FRAM offers virtually unlimited write endurance, which means it does not wear

out like other nonvolatile memory devices floating gate devices experience a

hard failure and stop writing in as little as IE5 cycles, making them unsuitable

for high-endurance applications.

FRAM operates without a change pump, enabling low power consumption of

floating gate devices, demand high voltage during write operations. FRAM

writes at the native voltage of the manufacturing process; 5V or even less or

more advanced process.

2.8 SCALABILITY STORAGE

Scalability storage is the ability of a system, network or process to handle a

growing amount of work in a capable manners or its ability to be enlarged to

accommodate that growth. A system whose performance improves after adding

hardware, proportionality to the capacity added, is said to a scalable system.

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3.0 DATA ANALYSIS

Data analysis is a process of inspecting, cleaning, transforming and modeling

data with the goal of highlighting useful information, suggesting conclusions

and supporting decisions making.

Data analysis can also be defined as the process of finding the right data to

answer questions, understanding the processes underlying the data, discovering

the important patterns in the data and then communicating the results to have

the biggest possible impact.

In this paper l will focus on how the different cadres of employees in an

organizations that is junior staff, middle level managers, senior managers, chief

executive officers/managing directors, board members and chairman of

companies/organizations can make use of data in taking decisions, consequently

the section of this paper will focus on Management Information System.

3.1 WHAT IS MANAGEMENT INFORMATION SYSTEM?

A Management Information System (MIS) is a computer based system that

provides the information necessary to manage an organization effectively.

Management Information System (MIS) is designed to enhance communication

among employees, provide an objective system for recording information and

support the organization‟s strategic goals and direction.

The system entails three primary resources. Information, Technology and

people.

3.2 OBJECTIVE OF MANAGEMENT INFORMATION SYSTEM

The objective of a Management Information System (MIS) system is to provide

useful information, data and analysis remains consistent but the features and

uses are customizable to suit the preferences and needs of every business,

individual or government for example, a government, without a profits focus,

can install a Management Information System (MIS) system that personally

tracks “customer” (auto licenses) as relates to their budgets.

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3.3 FUNCTION

The function of Management Information System (MIS) is to identify, manage,

and manipulate data (or groups of data) in a fashion that enables good decision

making.

In the first half of the 20th century, business manages information on paper, with

detailed filing systems and calculated reports. Cotemporary Management

Information System (MIS) involve one or more computers, working in concrete,

to achieve the stated goals of an organization. The function is always the same,

but the desired results fluctuate with the specific goals and needs of individual

organizations. Since the universal language of commerce is numbers, using the

incredible speed of computers, Management Information System (MIS) achieve

their function amazingly well.

3.4 TYPES

There are many types (and sub types) of management information systems as

there are business functions. Some of the most popular types of Management

Information System (MIS) are as follows:

Customer relationship management

Marketing, particularly target marketing efforts, directed of specific

groups of potential customers or selling niche products financial

managements.

Financial management

Strategic plan development

Inventory management systems

Optimal investing strategy creation

Projected sales volume

Projected operating expenses and cost control.

Other types of Management Information System (MIS) calculate project tax

revenue for governments‟ statistical evaluations of all types for business,

researchers and universities scientific purposes in all discipline; and cost/benefit

relationship for decision-making purpose.

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3.5 BENEFITS

The benefits of Management Information System (MIS) to businesses

governments, scientists, universities, students, non-profits and all other entities

are diversified. Some examples of the most often realized benefits include the

following:

Implementation of Management by objectives (MB) techniques:

Management Information System (MIS) allows all participants both

management and staff, to view, analyze, and interpret useful data to set

goals and objectives.

Generates competitive advantage: Business succeeds or fail based on

how they handled competitive challenges. Management Information

System (MIS) if implemented properly provided a wealth of information

to allow management to construct effective plans to meet, and beat, their

competitors.

Fast reaction to market changes: The victory often goes to the quick,

not necessarily the best; Management Information System (MIS) can

deliver facts, dash friends to businesses with lighting speed. Having this

information allows companies to react quickly to market changes,

regardless of the type (positive or negative of volatility.

3.6 CLASSIFICATTION OF MANAGEMENT INFORMATION SYSTEM

(MIS)

3.6.1 TRANSACTION PROCESSING SYSTEMS

Transaction processing systems are designated to handle a large volume of

routine, recurring transactions. Banks use them to record deposits and payments

into accounts. Supermarkets use them to record sales and track inventory.

Managers often use these systems to code with such tasks as payroll, customer

to suppliers.

3.6.2 OPERATIONS INFORMATION SYSTEM

Operations information systems were introduced after transaction processing

system. An operation information system gathers comprehensive data, organizes

it and summarizes in a form that is useful for managers. These types of systems

access data from transaction processing system and organize it into a usable

form. Managers use operations information system to obtain sales, inventory,

accounting and other performance related information.

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3.6.3 DECISION SUPPORT SYSTEM (DSS)

A decision support system (DSS) is an interactive computer system. They can

be used by managers without help from computer specialists. A DSS provides

managers with the necessary information to make informed decisions. A DSS

has three fundamental components:

Database management system (DBMS), which stores large amounts of data

relevant to problems the DSS has been designed to tackle, model based

management system (NBMS), which transforms data from the DBMS into

information that is useful in decision making and dialog generation as

management system (DGMS), which provides a user friendly interface between

system as the managers who do not have extensive computer training.

3.6.4 EXPERT SYSTEMS AND ARTIFICIAL INTELLIGENCE

Expert systems and artificial Intelligence use human knowledge captures in a

computer to solve problems that ordinarily need human expertise mimicking

hum expertise and intelligence requires the computer to do the following:

recognize, and learn from experience. These systems explain the logic of their

adire to the user; hence, in addition to solving problems they also can serve as a

teacher. They use flexible thinking processes and can accommodate new

knowledge.

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4.0 INTELLIGENT INFRASTRUCTURE SYSTEMS IN RAIL INDUSTRY

Railway development projects were put in place to respond to the shortage in

infrastructural resources, in order to meet growing demand for capacity (Dft,

2010). Crainic et al., (2009) have pointed out that building new

infrastructure to fulfill these demands is no longer an option. A more

optimal approach to infrastructure maintenance is therefore necessary and that

is to move from breakdown maintenance (fixing after failure) and time-

based preventive maintenance (fixing following a periodical inspection) to

predictive maintenance (fixing before failure).

Reliable sensors, sophisticated algorithms and advanced surveillance systems

have enabled live monitoring of the infrastructure in complex work

environments. This architecture has different names in various industries, such

as Condition Monitoring Systems in power plants (Hameed et al., 2009):

Condition Based Maintenance in mechanical systems (Jardine et al.,2006),

Structural Health Monitoring in aviation (Buderath & Neumair,2007)

Pervasive Healthcare in medical systems (Drew & Westenskow,2006).

Integrated information systems to support maintenance and monitoring have

long been used in different industries and domains. Some examples include:

manufacturing (Lau, 2002; Jardine et al., 2006), undersea and petro-chemical

(Strasunskas, 2006), space exploration (Park et al.,2006), civil

infrastructure (Aktan et al., 1998; Aktan et al., 2000), water and sewage

(Adriaens, et al., 2003), defence (Jones et al., 1998) and transportation (King,

2006; Lyons and Urry , 2006; Ollier, 2006; Khan, 2007; Blythe and Bryan,

2008).

Intelligent infrastructure is mainly considered as a means of centralizing and

integrating the support that is currently provided to infrastructure maintenance

by monitoring the condition of assets remotely. Potential failure or unnecessary

fixed-term replacements will then be prevented by providing relevant

information to the maintenance function. Another main use of intelligent

infrastructure in the railway is to facilitate optimal asset maintenance.

Currently, maintaining assets is performed through fixed schedules. This is

time consuming, costly and risky approach can be replaced by analyzing real-

time asset information and attending to track-side equipment only when

necessary. Therefore, intelligent infrastructure in rail was introduced to move

the railway, and especially its maintenance and engineering activities, from a

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„find and fix‟ mentality to „predict and prevent‟, and potentially to „design and

prevent‟ (Bint, 2008).

Figure 4.1 below shows a visualization of intelligent infrastructure on the rail

network. This is a simplified version of the model that NR applied to guide the

implementation of a pilot intelligent infrastructure system (Network Rail,

2009). The oval on the left hand side of the figure shows some of the

infrastructural assets (e.g. embankment, point, signal, level crossing, and track)

that can potentially benefit from a more optimal maintenance regime. Loggers

or other data acquisition devices collect information regarding these assets

(i.e. remote condition measuring). Data presented in current systems, such as

RCM systems, are presented in an integrated database. A strategic

infrastructure solution is then required to extract the optimum information and

present it to the appropriate operator

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Examples of rail Infrastructural

assets

Acquire data through

new data acquisition device or loggers

Rail track

Embankment strategic intelligent Infrastructure

Solution Level crossing

n

Point machine

Existing proprietary applications

Signal

FIGURE 4. 1: SIMPLIFIED HIGH LEVEL MODELLING OF INTELLIGENT

INFRASTRUCTURE IN NETWORK RAIL

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Capturing data and attributes from domain components;

attributes can refer to the environment in which the

component is located, its age, type, etc.

Jardine et al., (2006) pointed out that, in the first stage (data acquisition), two types

of data should be considered: event data and condition monitoring data. The event

data looks into what happened (e.g. breakdown, overhaul) and the condition

monitoring data measures the health status of the infrastructure. However, they

suggest that, despite the importance of collecting event data, it is often neglected by

developers who wrongly assume that the recording of only condition monitoring

data will suffice.

Patterns o f c o m p o n e n t behav iour are p r o d u c e d . This

c a n b e achieved through studying historical data or

experimental findings.

This stage includes data cleaning, to ensure that the data is relevant and error free,

along with data analysis. The data analysis is usually conducted through algorithms

and mainly includes signal processing, image analysis, time-domain analysis and

frequency domain analysis (Jardine et al., 2006).

Generate system diagnostics and prognostics , followed by the

analysis of recognized patterns.

In this stage, sophisticated algorithms are used to assist operators in diagnosing

faults and suggest rectifying procedures. Although much has been done in

developing and analyzing diagnostics information (Hameed et al., 2009), it is more

difficult to develop rectifying procedures and present operators with a number of

options. This can be related to the difficulty in understanding the behaviour of

assets and, in particular, lack of appropriate understanding of the situational

information associated with the failure.

Transfer diagnostics and prognostics to relevant operator

In order to ensure effective implementation of an intelligent infrastructure system,

data obtained from the infrastructure must be transformed into useful information,

as well as being exploited in the optimal way (Crainic, 2009). Failure to define the

correct purpose for the data may result in the system presenting too little

information or overloading the operator with inappropriate information. Hence, it

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is important to realize what level of detail is required. For instance, does the

operator require a simple binary (working / failed) assessment of the status of an

asset, or a sufficiently detailed measurement? Moreover, the operator should know

the effect of the measured condition on the overall run of the service in order to

predict potential failures and behaviours of the asset in the future.

Update the pattern log with new conditions.

This is the stage where information and knowledge captured in earlier stages are

now fed back to the system (e.g. using artificial intelligence, artificial neural

networks, or simply operator‟s feedback). However, eliciting knowledge from real-

world maintenance practice is not very straightforward and it is not easy to

document it digitally for future use (Jardine et al., 2006). One solution to facilitate

and support this feature is to develop a robust understanding of problem solving

and fault finding practice as well as operators‟ knowledge and information

requirements.

Aktan et al., (1998) conducted exploratory research to investigate the issues

associated with remote sensing of the asset conditions during live operations while

developing highway bridges. They confirm that, in doing so, a wide knowledge of

advanced sensors, communication and information technology, state parameters,

environment, deterioration mechanism and performance measures is required. Such

intelligent infrastructure systems should be able to:

Sense the definitive features on the piece of infrastructure

Assess the condition by analyzing the information captured

and performance criteria

Communicate the findings through appropriate interfaces

Learn from infrastructure condition patterns

Decide the optimum course of action

From this, they suggested three main factors to be considered in order to develop an

effective intelligent infrastructure system:

1. The knowledge required for diagnosing problems

2. The technology necessary for transmitting the knowledge

3. The people who will work with the technology.

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From the three factors identified by Adriaens, et al., (2003), technology is the least

problematic one especially with the advent of highly sophisticated algorithms,

artificial intelligence application and neural network algorithms, etc (Adeli &

Jiang, 2009). The other two factors (i.e. knowledge and people) are the most

problematic.

Therefore, one of the most important challenges facing the success of an intelligent

infrastructure system is the management of information within the system.

As mentioned in the previous sections, railway control systems have enabled the

control of large areas with complex intertwined components and have

revolutionised the look and functionality of control systems. It seems that

intelligent infrastructure aims to improve this functionality by managing and

integrating the existing technologies, thereby assisting operators to make more

informed decisions. However, the review of the potential domains of intelligent

infrastructure suggests a number of challenges that will be potentially even more

problematic with the introduction of intelligent infrastructure systems. These

include:

Information overload

Multi-agent control

Alarm handling

4.2 INFORMATION OVERLOAD

One of the recurring questions in designing dynamic control environments such as a

railway control is whether more information is better. Process and transport control

systems collect data remotely from complex environments, enabling operators to

monitor and intervene if necessary (Sheridan, 1992).

Within railways, advanced technologies, such as the switch from manual control to

automation, the introduction of highly reliable sensors and the application of

sophisticated algorithms, have increased the volume of data available to operators

in their decision making. While this creates opportunities for more efficient

control, it also places an increasing cognitive demand on the operator.

Similar research in complex environments has shown that operators are

disadvantaged by the provision of multiple sources of information as well as

multiple opportunities for actions (Omodei et al., 2005; Seagull et al.,

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Chapter Four

2001). Therefore, there should be a balance between the number of tasks for which

operators are responsible and the amount of information made available to them.

Cummings and Mitchell (2007) noted that there are limits to how much information

operators can keep track of before they demonstrate degraded performance.

4.3. MULTI-AGENT CONTROL

Control environments are moving more and more towards integration and

centralization. Therefore, the information generated in one control room will be

used in another. For example, in railway maintenance, the information presented to

the maintenance operator in the national control centre will be used by track

workers. Moreover, different operators are responsible for different aspects of a

decision making task. The cooperation between personnel with different roles

within the control environment is a key aspect of the success of these control

processes.

Two aspects of the work that should be analyzed in order to understand and

inform these multi agent control systems are:

1. What are the roles involved with these systems?

2. What are the goals and objectives of each of those roles?

For example, in railway intelligent infrastructure, if we assume that asset failure

prevention is the ultimate goal, the information provided by the system will be used

differently by different people (from the track worker on a railway site to the

operator in a control room and ultimately by the policy maker).

Hoc (2001) looked into the concept of cooperation between different agents in the

dynamic environment (e.g. interface, operators, etc.). He has recommended that one

way of developing an understanding of levels of coordination is to decompose one

goal (i.e. solving a problem) into its sub-goals and look into the activities of different

agents during each sub-goal. It is also important to understand the boundaries

between the different roles of intelligent infrastructure and to support each role

accordingly.

Information presented to operators should be in a cohesive way that matches their

mental model and cognitive processing that is necessary for effective decision

making.

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Chapter Four

4.4 ALARM HANDLING

Technical advances in designing complex control settings allow huge amounts of

data to be collected from various remote sensors. Presenting all of these data

seems to be both impossible and unreasonable. Alarms are then introduced to

assist human operators in managing these numerous sources of data.

When designing alarms, the following factors need to be considered in relation to

auditory displays include: appropriate level of sensitivity, contrast between the

audible siren and background noise, perceived urgency of the task and the

alarm respectively as well as multiple alarms (Robinson et al., 2006). These

factors, in their wider sense, refer to the two perspectives introduced by Woods

(1995): how informative the alarm is and alarm perception.

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Chapter Five

5.0 INFORMATION PROCESSING PARADIGM

The information processing paradigm is the outcome of a linear and

fragmented view of human-computer interaction, in which humans are

seen as information processors and it is possible to explore their activities

through investigating the information inputs (stimulus) and outputs

(response) (Rasmussen, 1986).

FIGURE 5.1: MODEL OF COGNITIVE ACTIVITIES OF RAILWAY

SUPERVISORY CONTROLLERS, TAKEN FROM EZZEDIN &

KOLSKI (2005)

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Chapter Five

Information processing paradigms have been widely used to facilitate studies

associated with decision making, problem solving, as well as alarm handling.

Models of alarm handling were introduced to guide the exploration of the

various stages conducted by operators when handling alarms; very early ones

include that of Lees (1983), which has three stages: detection, diagnosis and

correction. A model suggested by Rouse (1983) also has three stages:

detection, diagnosis and compensation. Although other models are

available, as noted by Stanton (2006), there is little evidence that these

models reflect a real life alarm handling environment. To overcome this

uncertainty, Stanton et al (1998) identified a sequence of activities that are

initiated by the generation of an alarm (Figure3.7)

This model includes two sets of events: routine and critical. When an alarm is

generated, operators observe the reported warning and accept if it is

genuine. Based on their understanding of a failure, operators might

analyze, correct, monitor, or reset the alarm. If the cause of the failure is

unknown, then the operator will conduct a series of investigations to

diagnose the problem. Finally, they monitor the situation to ensure that the

abnormality is dealt with (Stanton, 2006).

CONDITION MONITORING AND DIAGNOSTICS OF MACHINES DATA

PROCESSING, COMMUNICATION AND PRESENTATION.

PART 1

GENERAL GUIDELINES

1. SCOPE

This part of ISO 13374 establishes general guidelines for software specification

related to data processing, communication, and presentation of machine condition

monitoring and diagnostic information.

NOTE: Later parts of ISO 13374 (under preparation) will address specific software

specification requirements for data processing, communication, and presentation.

2. DATA PROCESSING

2.1 OVERVIEW

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Chapter Five

Relevant data processing and analysis procedures are required to interpret the data

received from condition monitoring activities. A synergistic combination of

technologies should establish the cause and severity of possible faults and provide

the justification for operations and maintenance actions in a pro-active manner.

A data processing and information flow of the type shown in figure1 is

recommended either on a manual or automatic basis, in order to implement

condition monitoring successfully. The data flow begins at the top, where

monitoring configuration data are specified for the various sensors monitoring the

equipment, and finally results in actions to be taken by maintenance and operations

personnel. As the information flow progresses from the data acquisition to advisory

generation, data from the earlier processing blocks need to be transferred to the next

processing block and additional information acquired from or sent external systems.

Similarly, as the data evolve into information, both standard technical displays and

simpler graphical presentation formats are needed. The flow progresses from data

acquisition to complex prognostic tasks, ending in the issuance of advisories and

recommended actions (one of which may be modification of the monitoring process

itself.

5.2 DATA-PROCESSING BLOCKS

5 .2.1. MACHINE CONDITION ASSESSMENT PROCESSING BLOCKS

Machines condition assessment can be broken into six distinct, layered processing

blocks. The first three blocks are technology-specific, requiring signal processing

and data analysis functions targeted to a particular technology. The following are

some of the most commonly used technologies in condition monitoring and

diagnostics of machines:

Shaft displacement monitoring;

Bearing vibration monitoring;

Tribology-based monitoring;

Infrared thermograhic monitoring;

Performance monitoring;

Acoustical monitoring;

Motor current monitoring;

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Chapter Five

Sensor/Transducer/Manual Entry

Figure1- Data-Processing and Information-flow blocks

The technology-specific blocks and the functions they should provide are as follows.

a) Data Acquisition (DA) Block: Converts an output from the

transducer to a digital parameter representing a physical quantity and

related information (such as the time, calibration, data quality

collector utilized, sensor configuration).

External

systems

data archiving

and

block

configuration

Technical

displays and

information

presentation

Data Acquisition (DA)

Data Manipulation (DM)

State Detection (SD)

Health Assessment (HA)

Prognostic Assessment (PA)

Advisory Generation

(AG)

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Chapter Five

b) Data Manipulation (DM Block): performs signal analysis, computes

meaningful descriptors, and derives virtual sensor readings from the

raw measurements.

c) State Detection (SD Block); facilitates the creation and maintenance

of normal baseline “profile” searches for abnormalities whenever new

data are acquired, and determines in which abnormality zone, if any,

data belong (e.g.). “alert” of alarm).

The final three blocks normally attempt to combine monitoring technologies in

order to assess the current health of the machine, predict future failures, and

provide recommended action steps to operations and maintenance personnel.

These three blocks and the functions they should support are as follows.

d) Health Assessment (HA) Block: Diagnoses any faults and rates the

current health of the equipment or process, considering all state

information.

e) Prognostic Assessment (PA) Block: Determines future health states

and failure modes based on the current health assessment and

projected usage loads on the equipment and/or process, as well as

remaining useful life predictions.

f) Advisory Generation (AG) Block: provides actionable information

regarding maintenance or operational changes required to optimize

the life of the process and/or equipment.

2.2.2 TECHNICAL DISPLAYS

To facilitate analysis by qualified personnel, relevant technical displays showing

data such as trends as well as associated abnormality zones are necessary. These

displays should provide the analyst with the data required to identify, confirm or

understand an abnormal state.

2.2.3 INFORMATION PRESENTATION

It is important that the data be converted to a form that clearly represents the

information necessary to make corrective-action decisions. This may be done in a

written format, numerically in order to demonstrate magnitudes, graphically in order

to show trends, or a combination of all three.

Chapter Five

Page 45 of 50

The information should include pertinent data describing the equipment or its

components, the failure type or fault, an estimate of the severity, a projection of

condition and, finally, recommended action. Cost and risk factors may also be

displayed.

2.2.4 EXTERNAL SYSTEMS

Retrieval of previous work histories from the maintenance system and precious

operational data (state/stop/loads) from a process-data historian is important in the

assessment of machinery health. After a health assessment is made, the maintenance

action to be taken may range from increasing the frequency of inspection to repair or

replacement of the damaged machinery or component. The effect on operations may

be an adjustment of operating procedures or a request to shutdown the equipment

immediately. This need for rapid communication to the maintenance and operational

system requires software interfaces to maintenance management system and

operational control systems. These interfaces are useful in order to communicate

recommended actions in the form of maintenance work requests and operational

change requests.

Page 46 of 50

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