12/2/12 Reliability engineering principles for the plant engineer 1/13 www.reliableplant.com/Read/18693/reliability-engineering-plant Reliability engineering principles for the plant engineerDrew Troyer, Noria CorporationTags: maintenance a nd reliabilit y , reliabil ity-centered maintenance Increasingly, managers and engineers who are responsible for manufacturing and other indu strial pur suits are incorporating a reliabi lity foc us into t heir strategic and tac tic al plan s and initiatives. This trend is affec ting numerous functional ar eas, inc luding machine/system design and procurement, plant operations and plant maintenance. With its origins in the aviation industry, reliability engineering, as a discipline, has historically been focused primarily on assuring product reliability. More and more, these methods are being employed to assure the production reliability of manufacturing plants and equipment – often as an enabler to lean manufacturing. This article provides an introduction to the most relevant and practical ofthese methods for plant reliability engineering, including: Basic reliability calculations for failure rate, MTBF, availability, etc. An introduction t o t he expon ential distribu tion – t he cornerstone of the reliabi lity methods. Identifying failure time dependencies using the versatile Weibull system. Developing an effective field data collection system. In troduc tion The origins of the field of reliability engineering, at least the demand for it, can be traced back to the point at which man began to depend upon machines for his livelihood. The Noria, for instance, is an ancient pum p t hough t to be the world’s first sophisticat ed machine. Utilizi ng hydrau lic energy from the flow of a river or stream, t he Nori a utilized buckets t o t ransfer water to troughs, viaducts and other distribution devices to irrigate fields and provide water to communities. If the community Noria failed, the people who depended upon it for their supply of food were at risk. Survival has always been a great source of motivation for reliability and dependability. While the origins of its demand are ancient, reliability engineering as a technical discipline truly flourished along with the growth of commercial aviation following World War II. It became rapidly apparen t to managers of aviation industry c om panies that crashes are bad for business. Karen Bernowski, editor ofQual ity Progress, revealed in one of her editorials research into the media value of death by various means, which was conducted by MIT stat istic professor Arnol d Barnett and repor ted in 1994. Bar nett evaluated t he number of New York Times front- page news artic les per 1,000 deaths by various means. He found that cancer-related deaths yielded 0.02 front-page news articles per 1,000 deaths, homicide yielded 1.7 per 1,000 deaths, AIDS yielded 2.3 per 1,000 deaths, and aviation-related acc idents yielded a whoppin g 138.2 articles per 1,000 deaths! The cost and high-profile nature of aviation related accidents helped to motivate the aviation industry to participate heavily in the development of the reliability engineering discipline. Likewise, due to the critical nature of military equipment in defense, reliability engineering tec hni ques have long been empl oyed t o assure operational readin ess. Many of our standards in the reliability engineering field are MIL Standards or have their origins in military activities. Reliability engineering deals with the longevity and dependability of parts, products and systems. More poignantly, it is about controlling risk. Reliability engineering incorporates a wide variety of analytical t echniques design ed t o help engin eers understand the failu re modes Related Articles How Ladders Enhance Safety, Reliability Information Management is Key to Maintenance Performance Using Overall Equipment Effectiveness Foster Teamwork for Better Results Resource Links Effective Maintenance Maintenance & Reliability Best Practices Book is Must Read for Plant Pro's. Store.Noria.com Oil A nalysis Tra ining Unlock the full potential of oil analysis. Noria.com Lubricati on Procedures Let Noria design your lubrication program and write your lubrication procedures. Noria.com Search: Home | Buyer s Gu ide | Glossar y | Ev ents | Bookstore | Newslette rs | Bl ogs | Br owse Topics MAINTE NA NC E EXCELLE NC E LE AN MANU FACTUR ING ENE RGY MANA GE ME NT WOR KPLACE SAFETY TALE NT MANA GE MENT OE E RC M
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Reliability Engineering Principles for the Plant Engineer
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7/30/2019 Reliability Engineering Principles for the Plant Engineer
Increasingly, managers and engineers who are responsible for manufacturing and other
industrial pursuits are incorporating a reliability focus into their strategic and tac tical plansand initiatives. This t rend is affec ting numerous funct ional areas, including machine/system
design and procurement, plant operations and plant maintenance. With its origins in the
aviation industry, reliability engineering, as a discipline, has historically been focused primarily
on assuring product reliability. More and more, these methods are being employed to assure
the production reliability of manufacturing plants and equipment – often as an enabler to lean
manufacturing. This article provides an introduction to the most relevant and practical of
these methods for plant reliability engineering, including:
Basic reliability calculations for failure rate, MTBF, availability, etc.
An introduction to the exponential distribution – the cornerstone of the reliability
methods.
Identifying failure time dependencies using the versatile Weibull system.
Developing an effective field data collection system.
IntroductionThe origins of the field of reliability engineering, at least the demand for it, can be traced back
to the point at which man began to depend upon machines for his livelihood. The Noria, for
instance, is an ancient pump thought to be the world’s first sophisticated machine. Utilizing
hydraulic energy from the flow of a river or stream, the Noria utilized buckets t o transfer
water to troughs, viaducts and other distribution devices to irrigate fields and provide water
to communities. If the community Noria failed, the people who depended upon it for their
supply of food were at risk. Survival has always been a great source of motivation for
reliability and dependability.
While the origins of its demand are ancient, reliability engineering as a technical discipline truly
flourished along with the growth of commercial aviation following World War II. It became
rapidly apparent to managers of aviation industry companies that crashes are bad for
business. Karen Bernowski, editor of Quality Progress, revealed in one of her editorials
research into the media value of death by various means, which was conducted by MIT
stat istic professor Arnold Barnett and reported in 1994. Barnett evaluated the number of New
York Times front-page news artic les per 1,000 deaths by various means. He found that
and patterns of these parts, products and systems. Traditionally, the reliability engineering
field has focused upon product reliability and dependability assurance. In recent years,
organizations that deploy machines and other physical assets in production settings have
begun to deploy various reliability engineering principles for the purpose of production
reliability and dependability assurance.
Increasingly, production organizations deploy reliability engineering techniques like Reliability-
Centered Maintenance (RCM), including failure modes and effects (and criticality) analysis
(FMEA,FMECA), root cause analysis (RCA), condition-based maintenance, improved work
planning schemes, etc. These same organizations are beginning to adopt life cycle cost-based
design and procurement strategies, change management schemes and other advanced tools
and techniques in order to control the root causes of poor reliability. However, the adoption of
the more quantitat ive aspects of reliability engineering by the production reliability assurance
community has been slow. This is due in part to the perceived complexity of the techniques
and in part due to the difficulty in obtaining useful data.
The quantitat ive aspects of reliability engineering may, on t he surface, seem complicat ed and
daunting. In reality, however, a relatively basic understanding of the most fundamental and
widely applicable methods can enable the plant reliability engineer to gain a much clearer
understanding about where problems are occurring, their nature and their impact on the
production process – at least in the quantitative sense. Used properly, quantitative reliability
engineering tools and methods enable the plant reliability engineering to more effectively apply
the frameworks provided by RCM, RCA, etc., by eliminating some of the guesswork involved
with their applicat ion otherwise. However, engineers must be particularly c lever in their
application of the methods because the operating context and environment of a production
process incorporates more variables than the somewhat one-dimensional world of product
reliability assurance due to the combined influence of design engineering, procurement,production/operations, maintenance, etc., and the difficulty in creating effective tests and
experiments to model the multidimensional aspects of a typical production environment.
Despite the increased difficulty in applying quantitative reliability methods in the production
environment, it is nonetheless worthwhile to gain a sound understanding of the tools and
apply them where appropriate. Quantitative data helps to define the nature and magnitude of
a problem/opportunity, which provides vision to the reliability in his or her application of other
reliability engineering tools. This article will provide an introduction to the most basic reliability
engineering methods that are applicable to the plant engineer that is interested in production
reliability assurance. It presupposes a basic understanding of algebra, probability theory and
univariate st atist ics based upon the Gaussian (normal) distribution (e.g. measure of cent ral
tendency, measures of dispersion and variability, confidence intervals, etc.).
It should be made c lear that this paper is a brief introduct ion to reliability methods. It is by no
means a comprehensive survey of reliability engineering methods, nor is it in any way new orunconventional. The methods described herein are routinely used by reliability engineers and
are core knowledge concepts for those pursuing professional certification by the American
Society for Quality (ASQ) as a reliability engineer (CRE). Several books on reliability
engineering are listed in the bibliography of t his article. The author of t his article has found
Reliability Methods for Engineers by K.S. Krishnamoorthi and Reliability Statistics by Robert
Dovich to be particularly useful and user-friendly references on t he subject of reliability
engineering methods. Both are published by the ASQ Press.
Before discussing methods, you should familiarize yourself with reliability engineering
nomenclature. For convenience, a highly abridged list of key terms and definitions is provided
in the appendix of this article. For a more exhaustive definition of reliability terms and
nomenclature, refer to MIL-STD-721 and other related st andards. The definitions contained in
the appendix are from MIL-STD-721.
Basic mathematical concepts in reliability engineeringMany mathematical concepts apply t o reliability engineering, particularly from the areas of
probability and st atist ics. Likewise, many mathematical distributions can be used for various
purposes, including the Gaussian (normal) distribution, the log-normal distribution, the Rayleigh
distribution, the exponential distribution, the Weibull distribution and a host of ot hers. For the
purpose of this brief introduction, we’ll limit our discussion to the exponential distribution and
the Weibull distribution, the two most widely applied to reliability engineering. In the interest
of brevity and simplicity, important mathematical concepts such as distribution goodness-of-
fit and confidence intervals have been excluded.
Failure rate and mean time between/to failure (MTBF/MTTF)
The purpose for quantitat ive reliability measurements is to define the rate of failure relative to
time and to model that failure rate in a mathematical distribution for the purpose of
understanding the quantitat ive aspects of failure. The most basic building block is t he failure
rate, which is estimated using the following equation:
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7/30/2019 Reliability Engineering Principles for the Plant Engineer
Figure 4. Failure Rate and the Cumulative Distribution Function
The declining failure rate portion of the bathtub curve, which is often called the infant
mortality region, and the wear out region will be discussed in the following section addressing
the versatile Weibull distribution.
Weibull Distribution
Originally developed by Wallodi Weibull, a Swedish mathematician, Weibull analysis is easily the
most versatile distribution employed by reliability engineers. While it is called a distribution, it
is actually a tool that enables the reliability engineer to first characterize the probability
density function (failure frequency distribution) of a set of failure data to characterize the
failures as early life, constant (exponential) or wear out (Gaussian or log normal) by plotting
time to failure data on a special plotting paper with the log of the times/cycles/miles to failure
plotted a log scaled X-axis versus the cumulative percent of the population represented by
each failure on a log-log scaled Y-axis (Figure 5).
Figure 5. The Simple Weibull Plot – Annotated
Once plotted, the linear slope of the resultant curve is an important variable, called the shape
parameter, represented by â, which is used to adjust the exponential distribution to fit a wide
number of failure distributions. In general, if the â coefficient, or shape parameter, is less than
1.0, the distribution exhibits early life, or infant mortality failures. If the shape parameter
exceeds about 3.5, the data are time dependent and indicate wearout failures. This data set
typically assumes the Gaussian, or normal, distribution. As the â coefficient increases above ~
3.5, the bell-shaped distribution tightens, exhibiting increasing kurtosis (peakedness at the
top of the curve) and a smaller standard deviation. Many data sets will exhibit two or even
three distinct regions. It is common for reliability engineers to plot, for example, one curve
representing the shape parameter during run in and another curve to represent the constant
or gradually increasing failure rate. In some instances, a third distinct linear slope emerges toidentify a third shape, the wearout region. In these instances, the pdf of the failure data do in
fact assume the familiar bathtub curve shape (Figure 6). Most mechanical equipment used in
plants, however, exhibit an infant mortality region and a constant or gradually increasing
failure rate region. It is rare to see a curve representing wearout emerge. The characteristic
life, or η (lower case Greek “Eta”), is t he Weibull approximation of the MTBF. It is always the
function of time, miles or cycles where 63.21% of the units under evaluation have failed,
which is the MTBF/MTTF for the exponential distribution.
7/30/2019 Reliability Engineering Principles for the Plant Engineer
Appendix: Select reliability engineering terms from MIL STD 721Availability – A measure of the degree to which an item is in the operable and committable
state at the start of the mission, when the mission is called for at an unknown state.
Capability – A measure of the ability of an item to achieve mission object ives given the
conditions during the mission.
Dependability – A measure of the degree to which an item is operable and capable of
performing its required function at any (random) time during a specified mission profile, given
the availability at the start of the mission.
Failure – The event, or inoperable state, in which an item, or part of an item, does not, or
would not, perform as previously specified.
Failure, dependent – Failure which is caused by the failure of an associated item(s). Not
independent.
Failure, independent – Failure which occurs without being caused by the failure of any other
item. Not dependent.
Failure mechanism – The physical, chemical, electrical, thermal or other process which
results in failure.
Failure mode – The consequence of the mechanism through which the failure occurs, i.e.
short, open, fracture, excessive wear.
Failure, random – Failure whose oc currence is predictable only in the probabilistic or
statistical sense. This applies to all distributions.
Failure rate – The total number of failures within an item population, divided by the t otal
number of life units expended by that population, during a particular measurement interval
under stated conditions.
Maintainability – The measure of the ability of an item to be retained or restored to specified
condition when maintenance is performed by personnel having specified skill levels, using
prescribed procedures and resources, at each prescribed level of maintenance and repair.
Maintenance, corrective – All actions performed, as a result of failure, to restore an item to
a specified condition. Corrective maintenance can include any or all of the following steps:
localization, isolation, disassembly, interchange, reassembly, alignment and checkout.
Maintenance, preventive – All actions performed in an attempt to retain an item in a
specified condition by providing systematic inspection, detection and prevention of incipient
failures.
Mean time between failure (MTBF) – A basic measure of reliability for repairable items: the
mean number of life units during which all parts of the item perform within their specified
7/30/2019 Reliability Engineering Principles for the Plant Engineer