Maintaining%20asset%E2%80%99s%20desired%20availability%20can%20be%20a%20daunting%20task%20but%20not%

Maintaining asset’s desired availability can be a daunting

task but not anymore (Part 1): A reliability approach

MAINTAINING ASSET’S DES IRED AVAILABIL ITY CAN BE A DAUNTING TASK BUT NOT ANYMORE (PART 1) : A REL IABIL ITY APPROACH

PAGE 2 OF 23

List of Contents

Summary 3

Introduction 4

What is Reliability Centred Maintenance (RCM)? 5

Reliability Centred Maintenance Model Deliverables 6

RCM Process 8

Reliability Engineering: Failure Model 10

Flexing PDM Inspection Frequency 13

Performance based Partnership 17

Virtual Tools – Performance and Data Analysis 20

Prognostic and diagnostic tools 21

Conclusion 22

References 23


PAGE 3 OF 23

Summary

This paper addresses some of the key disadvantages associated with conventional

calendar based maintenance, where an average client spends an extensive amount of

money on asset maintenance and in return the existing calendar based maintenance

model often offers poor visibility on asset’s operational status, availability and

savings. With very few options facility owners often seek to reduce their spending on

maintenance but without the appropriate performance indicators clients may face a

potential risk of losing visibility on their life critical assets operating conditions which

can often lead to unscheduled downtime, poor availability and surge in maintenance

cost.

Uptime Plus proposes a maintenance model which is an adaptation of both Reliability

Centred Maintenance and Performance based partnership model [1],[3],[5] aimed to

overcome the major disadvantages of conventional calendar based maintenance and

provide a cost effective solution to improve asset’s reliability, efficiency and most

importantly identifies Key Performance Indicators (KPIs). With the aid of condition and

performance monitoring tools clients can now have a broader spectrum of their

asset’s operational status consequently mitigating any potential failures at an early

stage where the cost of intervention is less.


PAGE 4 OF 23

Introduction

Infrastructure maintenance industry has

seen a tremendous growth over the span of

10 years, where the introduction of new

technologies have paved a way for clients to

increase their life critical asset’s reliability

and efficiency, but the cost of maintaining a

critical asset at a desired availability is still

high due to hours and the labour required to

perform the necessary maintenance tasks

and quite often clients and service providers

lose visibility of assets operational status

and other key parameters (reliability,

probability of failures, availability, failure rate

and Mean Time Failures). Considering the

recent economical climate, clients are often

forced to resort to more conservative

policies that would enable them to reduce

their spending on maintenance and label

some of the risks caused due to lack of

maintenance as “Accepted Risk” but in

reality it has an indirect impact on the

engineering resilience of the facility.

In addition to that, identifying spares for the

critical assets is a task that challenges both

parties (client/ service provider), where a

huge sum of money is spent on buying

those spares without any information on

asset’s operational status, In most cases the

acquired spares are either kept on site or at

a safe house, quite prepared for the

impending failures. It seems logical and can

be deemed as “Common Sense”, the

questions that needs to be answered is

“Does the asset really need a spare at this

point in time?” and “what happens if that

asset or some of its components becomes

obsolete?” Oversized inventory can lead to

insufficient use of the capital and can cause

serious impact on savings.

Clients can also have a tough time in

making decision regarding when to replace

an asset as in most cases they don’t have

any site/field information that tells them how

well their critical asset is currently

performing, and usually in the facility

maintenance industry asset replacement is

carried out on “better safe than sorry”

principle, which again sounds appropriate

but the policy is not suitable for all assets.

The primary objective for any maintenance

policy is to let the user know that the

deployed policies and procedures have

actually helped an asset to reach or extend

its documented life expectancy and maintain

the desired availability throughout its service

life.

Unscheduled downtime is the single

parameter that every individual in the

industry tries to avoid, every firm who offers

critical infrastructures services have their

own proactive measures to mitigate the

failures but only a handful of them

acknowledges the facts that unscheduled

downtimes are inevitable and reliability

studies have shown over 20% of asset

failures are age related. It is imperative to

clearly understand the deployed policies &

procedures should focus on finding the

optimum inspection interval. The traditional

approach towards this issue is performing

preventive inspections/tasks at predefined

frequencies (i.e. Monthly, six monthly,

yearly), now that sounds like the right option

but what if the actual maintenance activity is

itself the root cause of the asset failure? In

FM these failures are known as

“Maintenance Induced Failures*”. [See footnote]

*Maintenance Induced Failure: Type of failure occurs when a maintenance technician performs an intrusive inspection or service on equipment and induces or causes a failure.


PAGE 5 OF 23

What is Reliability Centred Maintenance (RCM)?

The term “Reliability Centred Maintenance”

has various definitions and the most suitable

one is defined as “The process used to

determine the maintenance requirements of

any physical asset in its operating context”

[1], In simple words, it means the

maintenance tasks are performed only when

its required by identifying failure modes for

the particular asset and collating ages to

failure data to determine Predictive (PdM)

and Preventive (PM) inspection intervals. It

is a method that identifies applicable and

effective maintenance tasks required to

maintain the inherent reliability of an asset

with minimum cost.

This methodology has been widely

acknowledged in the process industries,

hospitals and by the aviation manufacturers

where reliability and business continuity is

the life line for these industries and any

unscheduled downtime can cause serious

financial or health & safety impact. In an

ideal world, RCM should be a key process in

facility maintenance (FM) industry but

unfortunately FMs perspective towards RCM

is not very optimistic as it has been

classified as a “complex” process with too

many “variables” involved and the most

common response for not leaning towards

this methodology is the myths that

surrounds around RCM.

Myth #1: “Operational cost is HIGH

during RCM implementation”

The answer is YES, but it is a one off cost,

in RCM world this surge in the operational

cost is called “Start up Cost” which is

caused during hardware acquisition process

i.e. buying the tools and setting up training

required for the engineers to implement and

maintain the desired RCM standards. It has

been proven and acknowledged by the

reliability engineering community, the Return

on Investment (ROI) from RCM is on

average between 25%-30% [1]

Myth #2:“Sounds good in theory but in

reality performing less maintenance in a

critical assets poses threat to asset’s

operational efficiency and it is vulnerable

to failures”

Surprisingly NO, over 80% of asset failures

are not due to age therefore performing

conventional calendar based maintenance,

replacements or overhauls do not increase

asset’s reliability, In addition to that

performing calendar based maintenance

might increase the risk of maintenance

induced failures which are often hidden.

Failure rate of an asset subjected to RCM is

far less compared to an asset that

undergoes conventional calendar based

maintenance because sometimes “Less is

more”.

Myth #3: “Replacing PPM tasks with

predictive inspections has a negative

impact on the maintenance model”

Performing predictive tasks does not replace

the original preventive tasks; it is an efficient

decisive method that allows engineers to

identify when to perform the specified

intrusive maintenance.


PAGE 6 OF 23

Reliability Centred Maintenance: Deliverables

Maintenance models are basically devised

to improve the operational efficiency of

assets, reduce downtime and enable facility

managers to allocate their resources more

efficiently by providing clear visibility on

asset’s KPIs, i.e. how well they are

performing at any given state?, Does

conventional calendar based maintenance

model deliver these aspects?

Sadly no, all it does is carry out tasks at a

regular interval i.e. constantly intervening

with the asset, and creates an optimistic

view that “failures are reduced or eliminated

because maintenance was carried out

before any failures could occur”, the

statement above is not aimed to dismiss

preventive maintenance strategy (PM) and

say “It is all wrong”. PM is the most essential

aspect in asset maintenance and its full

efficiency can be only achieved, if it’s utilised

in part. Constantly intervening with an asset

acts as a catalyst during asset deterioration

process and in most cases makes assets

prone to premature failures.

The proposed maintenance model is a

fusion of performance based partnership

model [refer section 3] and reliability centred

maintenance, where the latter is used to

identify the failure modes, key performance

indicators and reliability parameters via ages

to failure data and the former is used to

identify the minimum maintenance

conditions and the PM and Pdm tasks

intervals to meet its specified performance

level. By combining the two maintenance

models, some of the major disadvantages of

performance based partnership approach

such as loss of flexibility and the ability to

deal with changes is mitigated as the model

is devised to evolve constantly based on its

performance.

Ages to failure Data

FMEA

Predicitve

Inspection

PPM

RCM

Performance

Availability

SavingsReduced

Downtime

Reliability

parameters

Figure 1: Graphical representation of RCM inputs and service outputs.


PAGE 7 OF 23

The model allows the user to identify Key

Performance Indicators (KPIs) on critical

assets by identifying the possible causes

that could affect the specified KPIs. For

example, consider an 11KV transformer

required to be kept at an availability of

99.98%, the first step in identifying the KPI is

to perform FMEA analysis [8] and identify

the possible failure modes that can occur in

that transformer based on its current

operational context. Once failures modes

are identified, select appropriate

maintenance tasks (PdM, PM) to be

performed at appropriate intervals. In this

case, KPIs for an 11KV transformer will be

the secondary voltage (tolerance of ±5%),

cooling, winding temperature and Insulation.

Based on these parameters a minimum

operating condition can be devised which

provides a clear objective for the engineers

that would allow them to address some of

the key questions:

- How the asset should be

maintained?

- The level of maintenance required?

- What are Key Performance

Indicators (KPIs) to be monitored?

- What are the potential causes that

could affect the specified KPIs?

The model is aimed to

- Improve the performance of critical

assets

- Increase asset availability and

reliability

- Reduce asset downtime

- Increase cost savings

- Optimise asset replacement

strategy

- Identify hidden failures and monitor

current use of time and resources

Figure 2: Typical Performance monitoring chart

Asset No: TX2123355

0

20

40

60

80

100

Month

Performance Scale

2010

2011

2012

2010 50 50 50 80 80 80 90 90 90 90 90 90

2011 70 70 70 90 90 90 70 70 70 90 90 60

2012 90 90 90 80 80 80 60 60 60 60 60 50

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Threshold limit-70


PAGE 8 OF 23

RCM Process

A. Asset classification

Asset classification is the most essential

process in implementing RCM, where it

needs to be thorough and assets should be

classified in any one of the two types. The

whole idea behind this process is to use the

time and resource efficiently and clearly

identify level of maintenance tasks required

based on asset’s criticality (Business and

Functional).

Type Criteria

Critical 1-1 Important to business function and continuity where

the user can’t afford for unscheduled downtimes.

1-2 Asset’s failure can induced failures to other critical

asset connected to it.

Non

Critical

2-1 Does not pose serious threat to business continuity

and does not incur financial loss.

2-2 The user can afford for unscheduled downtime.

2-3 Assets with random failures*

2-4 User can afford run to fail.*

Table 1: Asset Classification Criteria

B. Failure Modes and Effects Analysis (FMEA)

Failure Modes and effects analysis (FMEA)

is a form of reliability study that identifies

possible failure modes in an asset which in

turn enables the engineers to decide the

appropriate maintenance tasks that can be

of predictive or preventive in nature that

would enable them to mitigate possible

failures modes.

- Failure Modes - (what could go

wrong?)

- Cause (what could cause those

failure modes?)

- Effects - (what is the consequence?)

Once these elements are identified, each

failure mode will be rated from [1 – 10] for

their Severity, Likelihood of Occurrence and

Likelihood of Detection based on asset

history & available condition monitoring

tools, then the Risk Priority Number (RPN)

can be calculated based on eq (1), The RPN

provides a clear indication on failure modes

that are critical and has high probability of

occurrence based on the scale mentioned in

table [2]

RPN = Severity x Occurrence x Detection ………………………………… Eq (1)

Scale Status

RPN = 0- 25

RPN = 26- 125

RPN>125

Table 2: Generic RPN scale

*- Assets with random failure patterns, where it is no longer is cost effective to maintain it can also be classified as rogue asset


PAGE 9 OF 23

C. Failure Pots

The term “Failure Pots” refers to ages to

failure data, which is crucial for calculating

the failure distribution and reliability

parameters. The process of collating ages to

failures data is a continuous process and

accurate predictions can be obtained if it’s

constantly updated and returned to the RCM

facilitator on a monthly basis.

Why do we need it?

Every failure has a pattern; in order to

identify the failure pattern the engineers

should have the visibility of “when the asset

failed?”, “what caused the failure?” and

“number of occurrences?” The possible

cause of failures can be identified by

performing FMEA analysis

Asset PUMP#23 Function Pumps cold water to the chillers 2 & 3

Failure modes F1 F2 F3 F4 F5 F6

Number of Failures 2 0 1 1 3 0

Time (hours) 25000 33000 37000 37500 23900 50000

Table 3: Typical Ages to failure data

The above table contains sample ages to

failure data of a pump, where F1 to F6

represents the failure modes as mentioned

in the table [4] and time (hours) indicates the

time those failures were detected or

occurred, and the specified failures modes

are not limited since FMEA is meant to be a

continuous process

.

Failure Modes Causes

F1 Damaged Impeller

F2 Bearings

F3 Cavitations / Clogged suction pipe

F4 Excessive Loads, Overheating, Lubricant failure, corrosion

F5 Excessive vibration

F6 Age

Table 4: Typical Failure Modes

Failure Distribution

0

10000

20000

30000

40000

50000

60000

2 1 1 1 3 1

Number of Failures

Ho

urs No.of.failures

Failure Modes

Figure 2: Typical Failure distribution


PAGE 10 OF 23

Reliability Engineering: Failure Model

Calculating reliability parameters and

predicting asset failures [2] is often deemed

as “A time consuming” process but in reality

it is not that hard once the process for

acquiring ages to failure (i.e. raw data) has

been laid out. The two key parameters

required to perform failure predictions are

the number of failures and the time it was

detected or occurred. The process is aimed

to shed some light on following the

questions:

- When an asset is going to fail?

- What is time interval between two

successive failures for a particular

asset?

- What is the reliability of the asset?

- How much time do I have to perform

the remedial actions?

Failure Rate (λ):

A failure rate is the ratio between number of

failures occurred and the time at which they

were detected, it is usually denoted in

failures per year, it is crucial to understand

what is a failure* and what are the

assumptions?, [see footnote]

λ=R/ T…….. Eq (2)

Where,

R – Number of failures

T – Sample time or Operational time

it was detected

Mean Time between Failures (MTBF)

It is one of the most misunderstood

variables in RCM, as it is often confused

with assets life expectancy. It is defined as

the mean or average time between two

successive failures. The simplest method to

calculate MTBF is mentioned below,

MTBF=1/λ…Eq (3)

Where, λ – Failure rate, refer [eq (2)]

Failures

Average time between two successive failures

FA MTBF FB

Time

Figure 3: Showing Interval between two successive failures FA and FB respectively *The term “Failure” is defined as the termination of the ability of the asset as whole to perform its required

function, termination of the ability of any individual component to perform its required function but not the

termination oft he ability of the asset as whole to perform.


PAGE 11 OF 23

Reliability [R (T)]

It is the measure of resistance to failure of

an asset and it is directly proportional to

MTBF or failure rate

R(t)=e–t/MTBF

…Eq(4)

Figure 4: Snap shot of reliability parameters dialog box from the Predictive Maintenance

Management (PMM) tool

Probability of Failure [F (t)]

It is a measure that indicates the

unreliability of an asset based on its failure

data, the output basically denotes whether

the likelihood of failures will increase or

decrease at any given time.

F (t) = 1- R (t)….Eq (5)

Annual Failure Rate (AFR)

This parameter calculates failure rate for a

group of assets that are operational 24 x 7

and has same functional objective, it is

denoted by

AFR = Failures in the sample period x (52

weeks/ Number of weeks in the sample

period)

Number of Units in the population


PAGE 12 OF 23

2.1 Maintenance Review

As mentioned in the earlier section, RCM is

an optimum mix of predictive, preventive

and reactive maintenance, it is vital that this

principle is reflected on the maintenance

planner by performing a maintenance review

and identify the most appropriate method of

maintenance activity required for an asset.

- Acquire the list of maintenance

tasks.

- Identify and exempt the tasks that

are required to satisfy health and

safety legislations which can only be

performed by intrusive maintenance

from the review.

- Factor FMEA results

- Identify the tasks that can be

subjected to predictive Inspections.


subjected to preventive

Maintenance


subjected to visual Inspections

- While amending or assigning the

frequencies for PdM inspections, the

type of the condition monitoring test

and objective of the original PM task

should be considered, for example –

If a maintenance task aimed to

verify whether there are any

excessive vibrations in a pump on a

yearly basis, a non intrusive

vibration analysis is preferred and

recommended to be performed on a

six monthly basis, as the cause of

excessive vibration in any rotary

asset can grow rapidly thus

increasing inspection frequency will

enable engineers to keep track on

the operational status of the asset

and perform remedial works before

it the exceeds specified tolerance

level.

- It is important that the assigned

frequency should be feasible and

cost effective; the entire

maintenance review should be

performed by the RCM team since

most of the decisions are made

based on engineers/managers field

experience.

Mean Time to Repair (MTTR)

It is the measure of the average time

required to repair a failed asset or its

components and it is usually expressed

in hours

MTTR = Total Downtime in hours .Eq (6)

Number of Breakdowns

Availability (A)

It is generally defined as the degree to

which an asset and its component are in

operable and committable state at any

point in time when it is needed.

A = MTBF/ MTBF+MTTR ….Eq (7)


PAGE 13 OF 23

Flexing PdM Inspections Intervals

Maintenance models often don’t allow the

user to change the inspections interval as it

could have perceived impact on the cost,

availability or resilience of the facility. The

proposed RCM model allows “Flexing” the

PdM inspection interval based on the

following parameters:

- Asset’s Failure Pattern

- Age

- Operational life

Failure pattern of an asset is dependent on

the failure rate, and there isn’t a definitive

pattern for all assets, it varies based on the

load, environmental condition, temperature,

design, shipping, and installation. But most

assets follow a failure pattern called

“Bathtub-Curve”.

Failure Rate

Time

The curve itself is classified into three

phases and it is dependent upon “shape

parameter” denoted by the symbol β (Beta),

If β < 0, it is classified as Phase 1 (Infant

Mortality) or asset prone to early failures.

If β =1, it is classified as Phase 2

(Operational life) which indicates the asset

entered into its operational life or useful life

and prone to random failure and finally if β >

1, it is classified as Phase 3 (Wear out

Period) which indicates engineers that the

asset has high probability of failing and

suitable replacement or remedial actions is

required. The ages to failure data is again

crucial to calculate these parameters, based

on the β value engineers can schedule or

decide the appropriate maintenance tasks,

in most cases the PdM inspection frequency

will be increased in order to monitor the

status of the asset, where it gives sufficient

time for the engineers to organise the

remedial actions. The analysis works well for

assets that are operational 24 x 7, but for

critical back up assets (for example a

standby generators) the probability of failure

can be calculated by

Qn = 1 – exp [(n-1) * τ-γ] / n * exp [-[nt- γ / n] β

………..Eq (8)

Where,

Qn= Probability of Failure over the entire interval n; η = Characteristic Life Parameter;

β = Shape Parameter; γ = Location Parameter; τ = Inspection Interval

n = Number of times the component operated in it s life.

Phase 2: Operating Life

Phase 1: Infant Mortality Phase 3: Wear Out Period


PAGE 14 OF 23

Weibull Analysis

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 500 1000 1500 2000 2500 3000 3500

Hours

Be

ta

CDF= 0.632

CDF= 0.632

Scale parameter

= 2200hrs

Beta

= 1.71

Figure 6: Typical Weibull Probability Plot (based on the sample data)

P-F Curve

It is commonly defined as “A visual

representation of the behaviour of an asset

as it approaches failure”; The P-F Curve is

plotted against two parameters asset

condition and time. Once a failure has been

identified (Via PdM or Visual Inspection) it is

labelled as Point ‘P” called Potential Failure,

which means the asset or its components

had shown an early sign of deterioration and

it can lead to the Catastrophic or Functional

Failure point ‘F’ where an asset can no

longer be in operation or can no longer

perform its specified function

Usually the potential failures become visible

at around 70% of asset’s operational life,

and the interval between potential failure

and functional failure is called as “P-F

Interval”. The general rule is, during the P-F

interval the asset must be inspected at least

once; the inspection can be predictive,

intrusive/visual.


PAGE 15 OF 23

Figure 7: Typical P-F Curve

The Inspection interval (I) can be calculated by,

I = PF/n…Eq (9) [see footnote]

Where,

PF = Duration or Interval between Potential

Failure and Functional Failure

n = Number of inspection carried out during

PF Interval

*Example: If PF Interval = 8 years, Minimum number of inspections carried during the PF interval is 2 I =PF/N = 8/2 = 4 or 4 monthly

Based on the asset’s failure and

deterioration pattern, the predictive

inspection frequency is varied. Intrusive

maintenance is performed only when the

asset operational condition is in amber to

red transition period, i.e. the optimum point

of intervention and the maintenance interval

is tuned accordingly so that engineers do

not lose the visibility of the source. It is a

manual process and it’ll be usually be

carried out by a PPM manager based on the

reliability and the field information provided

by the RCM facilitator

The failure pattern illustrated below is a

typical bath tub curve, but it is very unlikely

that all the assets will follow this pattern as

there are six different types of failure

pattern. As discussed in the earlier section,

based on the ages to failure data asset

specific failure pattern can be identified and

can be used during the flexing process. [See

note]

OOPPEERRAATTIINNGG AAGGEE//TTiimmee

22000099

PPOOTTEENNTTIIAALL

FFAAIILLUURREE SSyymmppttoomm 11 DDeetteecctteedd 22001111

CC

PPddMM IInnssppeeccttiioonn IInntteerrvvaall ((II)) == 66

MMoonntthhllyy

IISS TTAASSKK IINNTTEERRVVAALL

PPRRAACCTTIICCAALL? = Yes

I

22001100

BB

PPFF Interval

22001122

FFuunnccttiioonnaall

IInnccrreeaassee tthhee iinnssppeeccttiioonn iinntteerrvvaall ttoo 66 mmoonntthhllyy ttoo eexxaaccttllyy pprreeddiicctt PPooiinntt ‘‘FF’’ aanndd aallssoo iitt iiss

mmoorree ffeeaassiibbllee aanndd ccoosstt eeffffeeccttiivvee aapppprrooaacchh


PAGE 16 OF 23

Asset Condition

Asset Condition Curve

PdM Inspection Interval

Time

Figure 8: Predictive Inspection interval (PdM) flexing

* Asset’s MTBF is not factored during PdM inspection interval flexing, as in most cases manufacturers MTBF and

asset operational MTBF will not be the same

Operating Life

Infant Mortality Wear Out Period


PAGE 17 OF 23

Performance Based Partnership – Business Model Performance based partnership /

Performance based maintenance [4],[5],[6] is

a maintenance approach where Uptime Plus

acts as an engineering consultant and takes

full responsibility in maintaining the condition

of all the assets in the facility within the

agreed budget. In this approach the

performance standards are agreed instead of

maintenance techniques consequently

shifting the risk from client to Uptime Plus

prior to contract agreement.

The performance requirements for this

approach can be divided into qualitative and

quantitative requirements where the former

implies that the client needs are expressed in

the form goals and objectives which are

usually derived from the functional and

performance requirements, the latter implies

the standard verification methods (audits).

The performance of a critical infrastructure

can be determined by its asset’s condition

and deterioration rate where this approach

predominantly focuses on condition based

maintenance or monitoring tools which would

enable the engineering consultant to have

the full spectrum of an asset’s operational

information.

Performance requirements are not just

technical, the performance of service

delivery (e.g. Response time) is also

accounted. The flow chart depicted in figure -

9 is a visual representation of the

performance based partnership approach

and the objective of this model is to improve

the quality & reliability of the assets, make

cost savings and provide budget certainty

and development of a long term relationship.

In the initial stage, client will liaise with a

group of maintenance contractors where in

this stage Uptime Plus will act as an

engineering consultant and contribute to the

planning process in which the maintenance

intervals are predetermined, and proposes

bespoke maintenance strategy within the

constraints of performance requirements.

Key Performance Deliverables:

- Improve asset’s reliability and quality

- Aid client to achieve direct cost

savings

- Reduce risks associated with

compliance and legislation.

- Provide clear visibility of asset’s

operational status

- Manage and monitor the

performance of life critical assets

- Being innovative in developing new

maintenance strategies.

An engineering consultant will take the

responsibility for providing evidence of

business related financial risks associated

with various maintenance scenarios. For

example, when the consultant reports a

defect or deterioration on a critical asset, e.g.

an 11KV transformer, client will be supplied

with the following information

- Type of fault

- Source of fault

- Ages to failure date (In visual form)

- Deterioration rate

- Time to fail

- Remedial and replacement strategy


PAGE 18 OF 23

During the performance specification phase,

both the client and consultant would liaise

with one another and decide the

performance threshold level for all critical

assets, i.e. they will decide the required

availability for the assets and the minimum

level of maintenance conditions. Business

continuity and the impact caused due to

failures are the decisive parameters for this

process, i.e. “How important is that asset to

my business?” and “what will be the impact

on business, if it fails?” and this task can be

time consuming and the role of an

engineering consultant is to help the client to

conclude a general agreement during this

process by providing an universal table of

critical assets, desired availability, key

performance indicators (asset specific) and

inspection interval to monitor the

performance.

Key performance indicators and the level of

maintenance required to satisfy the

specifications will be derived by Uptime Plus

facilitators. After the agreement, a bespoke

maintenance model will be devised and sent

to the client for final approval. In the

execution phase, completion of each task will

be reported back to the client where all the

tasks and its execution frequency will be

monitored and assessed by the client. In the

assessment period, the audit results of both

parties will be compared and evaluated

whether to confirm the Service Level

Agreements (SLA) and the specified

performance requirements which were

agreed during the specification phase are

met.

Asset Performance Indicator

API = [Conditional assessment of

the asset x 10] …eq [10]

Where the rating interprets [see foot note],

>80 API – Asset at good or high service level

70 > API < 80 – Asset at marginal condition

60 > API < 70 – Asset at deteriorating

condition

API < 60 – Asset at poor or critical condition

Asset Performance Indicator

Asset Number 122675 Description Packaged Chiller Unit Supports

Ist Floor COMMS room

Interval First quarter Second Quarter Third Quarter Fourth Quarter

2010 50 50 50 80 80 80 90 90 90 90 90 90

2011 70 70 70 90 90 90 70 70 70 90 90 90

2012 90 90 90 80 80 80 60 60 60 60 60 60

Table 6: API table

* - Asset performance indicator less than 70 requires preventive /intrusive maintenance


PAGE 19 OF 23

Long term agreement process:

Specify

Maintenance

requirements

Conclude General

Contract

Determine

starting

Point

Devise

maintenance

scenarios and

performance

criteria

Conclude Partnership

Agreement

Conclude

performance and

maintenance interval

Agree performance

guarantees

Evaluate the

partnership

Collate Project

Information

Project assessment

Condition

assessment

Devise maintenance

plan

Devise activity plan

Devise project plan

and PPM

Execution of work

Completion of work

Periodic

performance audit

Adjust maintenance

scenarios and activity

plan

Budgeting

maintenance project

Specify

provisional

performance

criteria

Supervise the

process

Assessment of

completed tasks

Assessment of

performance

indicators

Figure 9: Performance based Partnership flow chart

Client Uptime Plus


PAGE 20 OF 23

Virtual Tools – Performance and Data Analysis Performance monitoring tool is the most

essential aspect in the model, where all the

ages to failure data, current operational

status, asset information and their hierarchy

are stored. The tool allows the user to edit or

add an asset and provide a visual

representation of asset’s operational status

and keep track on the existing PPM planner,

remedial actions. In return the tool enables

the user to acquire the valuable historic data

that would allow the RCM facilitator to

determine asset’s failure pattern

consequently results in devising bespoke

maintenance strategy

Deriving bespoke asset replacement and

critical spares strategy is possible which is

usually based on the failure rate. Low MTBF

doesn’t necessarily means that the asset

should be replaced because asset

replacement is entirely age related not on

MTBF, having the historic information of

critical assets enables the user to distinguish

between MTBF and asset life expectancy,

resulting in optimising the existing asset

replacement model.

Figure 10: Snapshot of the PMM database and asset life expectancy window


PAGE 21 OF 23

Prognostic and Diagnostic tools

A quintessential aspect in both RCM and

performance based partnership approach is

the condition assessment. Visual inspection

is often the primary method to access the

operational status of the asset, but the

amount information that can be extracted via

this method is limited as there are

constraints that limit its efficiency (e.g.

human errors, spurious alarms).

In some cases, detecting failures can often

challenge even the most experienced

engineers since some of the early signs of

deterioration are hard to detect or almost

impossible during visual inspections. With

the rapid growth of sensors and signal

processing technology [9] engineers can

now have a much broader spectrum of their

asset’s operational status and allows them

to detect early deterioration signs and even

some of the hidden failures.

Thermal Imaging [7] (Thermography) is one

of the efficient non-intrusive methods to

detect any thermal anomalies on electrical

assets, and works on the principles of joules

heating effect, these heat signatures

increase when the current in a particular

conductor increases (overloaded) and it can

be easily be detected by Infra-red scanning.

It is suitable for detecting over-loads and

loose connections in fuses, switch gears,

transformers and bus bars.

In HVAC, thermal imaging is used to detect

refrigerant leaks, leaking pressure gauges

where the method can be used to replace

the quarterly intrusive leak detection checks

on chillers. In rotary assets, it is ideal for

locating the root cause of overheating. It is

suitable for identifying overheated bearings

or rollers, misalignment of shaft, pulley or

coupling and lubrication failure

Deterioration in fuel tanks, oil filled

transformers and pipe works can be

identified via fluid sampling that basically

detects any fluid contamination and provides

indication on the level of deterioration.

Partial Discharge (PD) is an electrical

discharge that does not completely bridge

the space between two conductors. The

discharge may be in a gas filled void, in a

solid insulating material, in a gas bubble, in a

liquid insulator. When partial discharge

occurs in a gas, it is usually known as

corona. Partial discharge is accepted as a

standard protocol test for high voltage assets

by power sectors. In addition to that partial

discharge detectors are equipped with

ultrasonic sensors where they are used to

detect arcing and corona in HV/MV

switchgears and transformers.

Vibration analysis is an efficient non-

destructive testing tool for the building’s

rotary assets, basically the tool analyses the

vibration signature of high speed rotary

equipments such motors, pumps which has

a on board diagnosis tool with the clever

algorithm that can prioritise repair

recommendations. The vibration analyser is

equipped with tri-axial accelerometer and a

two- point laser tachometer (speed

measurement) for precise vibration sampling

to identify bearings looseness,

misalignment, unbalance, gear problems

and bent shaft.


PAGE 22 OF 23

Conclusion

The proposed model provides complete transparency over critical asset’s KPIs and with aid of

reliability predictions supported by performance & condition monitoring tools, asset failures are

detected at an early stage where the cost of intervention is minimum, consequently enabling

facility owners to achieve substantial cost savings and enables them to maximise asset’s service

life and in certain case extends asset’s life expectancy. In addition to that asset’s failure pattern is

determined in order to identify the desired maintenance frequency consequently resulting in a

dynamic maintenance planner which is mapped against asset’s failure pattern, operational status

and age. This approach increase asset’s reliability, availability and maintains downtime well below

the threshold level. Overall the reliability and performance based approach for asset maintenance

is an effective replacement to the conventional calendar based maintenance.

About Authors:

Andrew Dutton Laxmi Vajravel

CEM Director, Integral UK, Critical Infrastructure Manager

1290, Aztec West 1290, Aztec West

Almondsbury, Bristol Almondsbury, Bristol

BS32 4SG BS32 4SG

Email: [email protected] Email: [email protected]


PAGE 23 OF 23

References

[1] Introduction to Reliability-Centred Maintenance by John Moubray, ISBN-10, 0750602309,

ISBN-13 9780750602303

[2] Paul Barringer, P.E, Predict Failures: Crow-AMSAA 101 and Weibull 101, Barringer &

Associates, Inc, Proceedings of IMEC 2004 International Mechanical Engineering Conference

December 5-8, 2004, Kuwait, Published by Kuwait Society of Engineers.

[3] Smith & Hinchcliffe, RCM--Gateway to World Class Maintenance, 1st Edition, 2003, Butterworth-Heinemann, ISBN: 9780080474137

[4] Ad Straub, Performance based Partnership forms for Maintenance by Dutch housing

Associations by, 2005, OTB Research Institute for Housing, Urban and Mobility Studies, Delft

University of Technology

[5] Igal M. Shohet, & Ad Straub, Performance-Based-Maintenance: A Comparative Study between the Netherland and Israel, 2010 EFMC (European Facility Maintenance Conference) [6] Ad Straub, The Maintenance Contractor as Services’ Innovator In Performance-Based Partnerships, TU Delft OTB Research Institute for Housing, Urban and Mobility Studies ,The Netherlands. [7] Business Focused Maintenance, Samples and Schedules by Jo Harris and Paddy Hastings, 2004, BSRIA 70174 December 2004 ISBN 0 86022 604 2 Printed by Multiplex Medway Ltd. [8] V. Narayan, Effective Maintenance Management – Risk and Reliability Strategies for Optimizing Performance, April 2004, Industrial Press Inc., ISBN 0-8311-3178-0 [9] Andrew K.S. Jardine, Daming Lin, Dragan Banjevic, A review on machinery diagnostic and prognostics implementing condition based maintenance, Mechanical Systems and Signal Processing, Volume 20, Issue 7, p. 1483-1510 [10] Alan Pride, CMRP, Reliability Centred Maintenance: http://www.wbdg.org/resources/rcm.php, last Updated on 06-07-2012.

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