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Practical Reliability Tools For Refineries And Chemical Plants

H. Paul Barringer, P.E., Barringer & Associates, Inc., P.O. Box 3985, Humble, TX 77347, Phone: 713-852-6810, FAX: 713-852-3749

Abstract

Reliability is the probability of equipment or processes to function without failure, when operated correctly, for a giveninterval of time, under stated conditions. Reliability numbers, by themselves, lack meaning for making improvements. Forbusiness, the financial issue of reliability is controlling the cost of unreliability from equipment and process failures whichwaste money. Reliability issues are understandable when converted into monetary values by using actual plant data. Severalreliability engineering tools are discussed.

Reliability DefinitionsReliability has many definitions (Omdahl 1988). Ifrepairs are not possible, reliability is the duration orprobability of failure-free performance under statedconditions. If repairs are possible, reliability is theprobability that an item can perform its intendedfunction for a specified interval under statedconditions. A single word definition for reliability is:dependability.

Reliability definitions include probabilities with valuesbetween zero (absence of reliability) and one(perfect reliability). Reliability is difficult becauseprobabilities change with time. Quantifying reliabilitysets the stage for quantifying risks when monetaryvalues are included.

Measuring the reliability of plants and equipment byquantifying the annual cost of unreliability incurred bythe facility puts reliability into a business context.Measurement, planning, and improvement are thingsbusinesses do well, but only when efforts arefocused on important problems.

Reliability ImprovementMotivationHigher plant reliability reduces equipment failurecosts. Failures decrease production and limit grossprofits. Boosting reliability, by reducing the cost ofunreliability, improves business performance. Theclear reason for reducing unreliability is spelled:money. The motivation for improving reliability isstraight forward for a business plan: Improve

reliability, reduce unreliability costs, generate moreprofit, and get more business.

We talk about reliability (a good word), but wemeasure failures (a bad word). Failures demonstrateevidence of unreliability with unfavorable costconsequences for businesses. Failures in mostcontinuous process industries are measured inprocess downtime. Cutbacks/slow-downs in outputare also failures. Failures require a clear definitionfor organizations making reliability improvements.Failures are loss of function when the function isneeded—particularly for meeting financial goals.

Everyone knows downtime stopping the productionprocess measures unreliability and defines a failure.Fewer people know that cutbacks/slow-downs inoutput are demonstrated failures. Define yourfailures to understand the need for reliabilityimprovements. Failure definitions (when attached todollars) galvanize organizations into action for makingimprovements.

Make reliability improvements pay their way byreducing the flow of funds into costs of unreliability.Find affordable business solutions. Avoid searchesfor perfection as business reliability solutions mustresult in profit improvements.

Can your plant afford a reliability improvementprogram? Answer these questions for your plant:• What is the annual cost of unreliability?• Where are losses occurring inside the plant?• Do you know the root cause for the losses?• What are the alternatives for reducing costs?

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• Are top ten Pareto items identified?--What are the specific items for correction?--Who is responsible for corrective actions?--What is the budget (time and cost) for change?

Answering these questions requires identifying theexpected annual failures occurrences (both obviousfailures and cutback/slow-down failures) and thencalculating the losses. These facts help motivatereliability improvement programs.

Reliability requirements for businesses change withcompetitive conditions and business risks—theplaying field is always tilting. Unreliability valueschange with business conditions. You don’t need thebest reliability in the world for your business—youjust need a cost advantage over your fiercestcompetitor. Even low cost industry providers needreliability improvement programs—reliability does notstand still. Unreliability costs usually increase.

Motivations for reliability improvements are driven bythe cost of unreliability—this tells the magnitude ofthe pain. Where the pain occurs within the plant isimportant. Why the pain occurs gets to the rootcause of the problem for corrective improvementprograms. Don’t rely on a magic bullet to fixreliability problems because seldom will correctingone item make a big change in overall reliability.

Reliability ToolsReliability tools showed their real value in the 1930s,40s, and 50s when used on exotic military programs.Fortunately many reliability tools do not need arocket scientist to use them cost effectively. Somesimple reliability tools provide big gains quickly anddefer the use of higher powered tools for squeezingout the remaining improvements. In all cases, scorecards for reliability improvements in business needmeasurements in dollars.

Use of reliability tools is evolutionary:• Start reliability improvement programs with

simple arithmetic and spreadsheets. Quantifyimportant cost and failure numbers.

• Gain momentum with good maintenancepractices. Improve teamwork using total

productive maintenance programs. Use rootcause analysis to efficiently solve problems.Learn and use a host of straight forwardreliability engineering tools to solve problems.

• Spur improvement programs by using statistics toquantify and understand scatter in the results.

• Build Monte Carlo computer models to simulateplant availability, reliability, maintainability,capability, and life cycle costs for decidingreliability strategies.

Keep all reliability scores measured in dollars.

The reliability improvement hierarchy shown inFigure 1 depends on having lower level programsoperational as precedents to accomplish higher levelobjectives.

G o o d M a i n t e n a n c e P r a c t i c e s

R o o t C a u s e

A n a l y s i s

T o t a lP r o d u c t i v e

M a i n t e n a n c e

REP

Figure 1: Reliability Hierarchy

The reliability improvement hierarchy uses a host ofnew tools to reduce both failures and the effects offailures. This work-process initiative is to gain abeachhead and expand territory by improvements.Few lasting improvements are maintained withoutimproving reliability as a work-process using fourimportant programs:

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• Good maintenance practices (GMP) uses thebest-of-class practices for “doing” activities.This requires having trained people at variouslevels with a commitment to on-going training.Use good procedures and practices to avoidcalamities. Teach new techniques and verify theworkforce has accepted them as requirementsfor correct performance—such as precisionalignment of rotating equipment along withprecision alignment of all piping and equipment.Start teamwork between maintenance andproduction departments.

• Total productive maintenance (TPM) is away of life for involvement of productionpersonnel into appropriate maintenance tasks fortender loving care of both equipment andprocesses. Aim TPM for effective use ofequipment and loss prevention by a preventivemaintenance effort with involvement of allpeople from top to bottom in the organization.Promote preventive maintenance through theTPM program by use of self-directed small workgroups.

• Root cause analysis (RCA) works on definingproblems into categories such as people,procedures, or hardware. Demonstrate RCAsolutions to problems will prevent recurrence,meet the organizations goal, and be within anindividuals control for preventing recurrence.

• Reliability engineering principles (REP) usesnew tools to solve old nagging problems. Theprogram is interested in solving the vital fewproblems that save/make the most money toreduce overall cost of unreliability. Many toolsuse bathtub concepts to match correct tools tocost effective strategies by applying science andengineering to reliability-centered maintenance(RCM) efforts.

Work-processes for reliability improvements start atground level with a firm foundation and builds towardgreater successes. Successful, on-going, reliabilityimprovement programs don’t just happen overnight—they are the results of well thought out programswhich are carefully implemented.

Generally, reliability work-process programs aredriven by the need for improved profitability. Theybegin by determining the cost of unreliability. Thisexercise requires plant failure data and cost data.

Age-to-failure data- Acquiring good data forequipment sounds easy. In many ways, it is adifficult task. The data acquisition task is as difficultas taking beautiful and salable photos with a camera.Photos are advertised as easy to acquire. However,few candid photos are sold and used in professionalliterature. Taking a beautiful photo requires:1. Good data logging equipment i.e., camera,2. Knowledge of art and science of photography,3. Careful illumination of the subject,4. Clear understanding of the photo’s purpose, and5. Photo appreciation for either art or commerce.

Failure of just one element results in a useless photo.Not all photos made by the experts are salable—most are junk. Trying to take a beautiful photo bysimply walking down the street taking snapshotswon’t produce good results. Similarly, acquiringphotos rapidly is equally unproductive. In short,taking a good photo requires a carefully constructedplan—it doesn’t just happen!

Abernethy (1996) says acquiring equipment failuredata has three basic requirements (items 1-3); andfor commercial businesses, add two other elements(items 4-5):1) Define an unambiguous time origin,2) Define a scale measuring the passage of time,3) The meaning of failure must be entirely clear,4) Measure cost consequences for failure, and5) Gain data analysis expertise for using data.

A thoughtful plan to acquire a few pieces ofcarefully logged age-to-failure data for equipment isbetter than vast quantities of poorly planned data.Notice the parallels between photography and failuredata.

People in many plants say they lack any data(Barringer 1995) when in fact, data is all aroundthem in various degrees of usefulness. Mostindustrial plants have been acquiring equipment

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failure data for many years and seldom is the dataanalyzed in a scientific manner. Rarely do peopleacquiring the data see the data used to solve theirproblems. The net result is vast data banks of nearlyuseless information acquired haphazardly andannotated poorly. Today’s task is to “mine” throughpiles of existing data while acquiring new age-to-failure data in a carefully thought-out manner so itcan be used for an economic advantage. The keyphrase to remember is “age-to-failure” and of coursethat requires a consistent definition of failure.

The field of reliability offers many technicalguidelines for how data should be acquired,annotated, and used for analysis. In many cases,failures need a “death certificate” just as occurs withhuman failures. Death certificates for humans havebeen so productive in producing analyzable results,that it now illegal in the civilized world for a person tobe buried without a death certificate listing age andcause of death.

Substantial amounts of failure data exist in theliterature awaiting knowledgeable use of the facts forimproving the reliability of plants and equipment(IEEE 1984) (RAC 1994) (Barringer 1993-readinglist). New data acquisition initiatives by the CenterFor Chemical Process Safety (CCPS 1989) usingproven skills of data analysis experts at Det NorskVeratas (OREDA 1992), are underway for acquiringdata from chemical plants and refineries. Don’twait. Start with common sense data from yourplants. Make failure data work for solving yourpersonal and company needs.

Many plants now know their mean time betweenfailure (MTBF) for pumps from special emphasis onreducing failures on rotating equipment. Because ofredundancy, pumps (and other rotating equipment)today seldom shutdown plants. However, non-rotating equipment often causes plant shutdowns—few plants know MTBF for heat exchanges,columns, reactors, and so forth. It is important to usefailure data to prevent failures and concentratesolutions on the vital few problems. For many plantsthe beachhead is secure in the rotating equipmentarea. It is time to reassign resources into non-rotating equipment areas (while holding gains in

rotating equipment areas) to reduce and preventfailures which cause plant outages.

Plant data for calculating failure rates is accumulatedin a simple form for evaluating the big picture inblocks as illustrated in Figure 2. Use a reasonablelength of time for the study period. Count thenumber of failures occurring during the time intervalto calculate MTBF. The failure rate is the reciprocalof MTBF. This effort for using plant failure datainvolves arithmetic—for example: MTBFC = (35,040hours)/(2 failures) = 17,520 hours/failure.

A B C Summary

Block Diagram Of Plant

StudyInterval

NumberFailures

MTBF

FailureRate

43,800 26,280 35,040 8,760 hrs/yr

1 3 2 1.7 failures/yr

43,800 8,760 17,520 5,153 hrs/fail.

22.8E-06 114.2E-06 57.1E-06 194.1E-06 fail/hr

Figure 2: Plant MTBF And Failure Rate

From the failure rate details, another spreadsheet isprepared to determine the lost time from the failureswhich leads to pricing the cost of unreliability.

A B C Summary

Block Diagram Of Plant

FailureRate

FailuresPer Year

CorrectiveTime PerFailure

Lost Time/yr

22.8E-06 114.2E-06 57.1E-06 194.1E-06 fail/hr

0.2 1.0 0.5 1.7

18 24 83 40.6 hrs

3.6 24 41.5 69.1 hrs

Figure 3: Time Lost From Unreliability

From Figure 3 it is clear all failures are not equal.Setting work priorities only on the basis of failure“body counts” can be misleading. It’s important todetermine average corrective times for failures tomake good estimates for total downtimes—lostproduction time for the plant is also lost money.

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Average times for corrective efforts can becalculated in the same manner as shown in Figure 2.

Generally several questions arise in using failure datafor calculating the cost of unreliability:• Will the number of future failures follow past

failure histories? The answer is generally yesunless heroic efforts have resulted in substantialimprovements in decreasing failure rates.

• Will future times for corrective actions to repairand restart the plant follow past histories? Yes,in general, and times for corrective actions areusually skewed data with tails to the right.

• Should planned turnarounds for renewal ofequipment be counted as failures? Yes—forinvestors, the plant is in a failure mode.

• Should turnaround costs be included in the costof unreliability? Yes—investors incur a doublehit for financial losses because the plant is notgenerating gross margin dollars and it is spendingmoney rapidly for the renewal process.

• What’s the advantage of starting at the “toplevel” of the plant to examine failures and makecost of unreliability predictions? The purpose forthe plant is to manufacture products andgenerate gross margins for the corporation evenwhen individual equipment fails. Think of theplant as an army, the army must move forwardin conquest knowing that individual soldiers willdie and the battle plan must consider the price tobe paid for its efforts—including concern aboutcasualties. Unreliability cost quantifies lossesexpected from failures and promotesconcentration on the vital few items to minimizelosses for failures.

Put the failure data to work calculating the cost ofunreliability as shown in Figure 4 based on lost grossmargin at $10,000 per hour, scrap disposal at $5,000per incident, and maintenance cost at $5,000 per hourof down time for breakdowns.

A B C Summary

Block Diagram Of Plant

Losses

Gross Margin 36,000 240,000 415,000 691,000

ScrapDisposal 1,000 5,000 2,500 8,500

Br’kdownMaint. 18,000 120,000 207,500 345,500

Total $ 55,000 365,000 625,000 $1,045,000

1

2

2 1

Figure 4: Cost Of Unreliability

This plant expects 1.7 failures per year which causesa cost of unreliability of $1,045,000 per year. Cell Cgenerates the greatest problem (60.2% of totallosses) and should rank at the top of the Paretodistribution for corrections by production-engineering-maintenance. Root cause reasons forthese projected losses must be determined asdescribed in the above. The number one priorityproblem for the site manger is to keep the plantrunning (66.7% of total losses).

From the failure data, two other important values canbe determined: annual availability and reliability asshown in Figure 5. High availability provides theopportunity to make money because the plant isready to respond. Low reliability provides theopportunity to incur outages which cost money.

In Figure 4, the cost of unreliability is a million dollarproblem! In the petrochemical industry, it is verycommon to find high availability numbers and poorreliability as shown in Figure 5 which entitles theplants to have a large cost of unreliability.

Plant Availability =

Plant Reliability = e-(λ*t) = e-(194.1E-06*8760) = 18.3%

Plant Unreliability = 1 - 18.3% = 81.7%

Uptime 8760- 69.1Total Time 8760= = 99.2%

Figure 5: Annual Availability And Reliability

Figure 5 also explains how plants benchmarked inSolomon reports can have superior quartileavailability and inferior quartile maintenance costs

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caused by numerous breakdowns. Examination ofthe reliability equation for Figure 5 shows chances ofmeeting a five year turnaround interval withoutfailures is only 0.02% chance for success—this plantwill pay a high price for outages requiring correctivemaintenance! Furthermore, inclusion of loss timefrom turnaround outages (whenever they occur) willpull down availability. Likewise inclusion of costlyturnarounds will also increases the cost ofunreliability.

How much can we afford to spend to fix theproblems causing unreliability? Using a rule ofthumb for one year payback, Figure 4 tells theexpenditure limits: Don’t spend more than $625,000to correct problems in Cell C or more than $365,000in Cell B.

The cost of unreliability index is a simple andpractical reliability tool for converting failure data intocosts. When the entire organization can understandthe problem on one side of a sheet of paper, effortfocuses on solving important problems.

Of course other reliability data can be converted intouncomplicated, figure-of-merit, performance indicesuseful for “getting a grip” on reliability by using meantimes between failure. Reliability is observed whenmean time between failure (MTBF) is largecompared to the mission time. Likewise, smallvalues for mean time indices, compared to themission time, reflect unreliability.

Accuracy of these simple indices are improved whenmany data points are screened using well knownstatistical tools described in IEEE, OREDA, andRAC publications referenced above. When only asmall volume of data is available it is best analyzedusing Weibull analysis techniques for componentfailures to arrive at better estimates of MTBF.

Probability plots-Failure data is chaotic because ofscatter in the data. Data scatter can be studiedarithmetically for first, quick look results, or refinedinto statistical details providing richer descriptionswhen converted into straight line plots of time-to-failure against cumulative chances for the failure.

Most engineers need graphical representation of datato fully understand problems. Without graphs,engineers are often overwhelmed by scatter as age-to-failure data provides no traditional X-Y facts formaking conventional plots.

Probability tools are growing in importance with theuse of personal computers which generate thecurves with ease (Fulton 1996). Weibull probabilitycharts are the tool of choice for reliability problemsbecause they often tell failure modes (howcomponents die):• infant mortality—use a run to failure strategy,• chance failures—use a run to failure strategy, or• wearout failure modes—consider a timed

replacement strategy based on costs.

Data from Weibull plots support RCM decisionsbased on highly idealized bathtub curves. (Moubray1992) Weibull plots tell component failure modes.

Weibull charts are particularly valuable for pointingcorrect direction for finding root causes of problemsusing a few data points. Larger quantities of dataadd confidence to the decision making process, but atconsiderable greater expense for acquiring bothfailures and data. The motivation for usingprobability charts is to understand failure data andreduce costly failures by appropriate correctiveactions. Consider the curves in Figure 6.

The slope of the line tells the failure mode is wearoutand the chances for failure can be read directly fromthe Y-axis.

In Figure 6, Pump B’s seal life is short. By standingand waiting for duty it experiences a service which is38.1/10.4 = 3.7 times more severe than Pump Abased on characteristic life values shown in thefigure. Perhaps a better strategy may be to swingthe pumps into service every other week rather thancontinue the current strategy of operation.

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1

5

2

10

20

30 40

50 60

70 80

90 95

99

1 10 100

O C C U R R E N C E C D F %

Pump Seal Life (months)

W/rr

WinSMITH Weibull Software Pump Seal Life Strategy

Eta Beta r^2 n/s10.402 2.151 0.983 10/0 Pump B

38.132 3.901 0.983 10/0 Pump A

Pump A--Primary PumpRun To Failure

Pump B--Back-up PumpRuns Only When Pump AFails

Figure 6: Weibull Probability Chart

Knowing the odds for success/failure is an importantfact for assessing risks. Probability charts are easilyinterpreted, and simple plots of probabilities multipliedby costs can be plotted against time to quantifydecisions and consider alternatives. In practice weseldom have too many data points for assessingrisks. Weibull plots use few data and help thedecision making process—some data is better thanno data for making cost effective decisions.

Monte Carlo models-Plant availability/reliabilitymodels are constructed starting with P&ID diagramsand predominate failure modes determined byWeibull analysis. Monte Carlo models use failuredata statistics along with computer generated randomnumbers to gain insight into economic improvements.

Some simple, but practical, Monte Carlodemonstration models are available in Excel™spreadsheets to provide ideas about how MonteCarlo models function. The demonstration modelsalso show end results for availability, reliability, andcost of unreliability when changes are made tocomponents of the model—these models can bedownloaded from the Internet. (Barringer 1996a).

Results from Monte Carlo models depend on goodfailure data (either actual or engineered data), goodrepair data, and accurate configuration by themodeler of how the plant physically operates. Ofcourse models are only approximations of plants and

making a model does not improve reliability—improvements must be converted into hardware.

Static spreadsheet model always produce the sameresults each time using arithmetic failure data.However equipment never fails at the same age-to-failure or repair times in real life. Thus Monte Carlomodels give different answers for availability,reliability, and costs each time the model is solved.

Now with today’s fast PCs and Monte Carlo models,thousands of solutions can be generated quickly atvery low cost to model thousands of years ofoperation. From the multitude of answers producedby the model come trends, plant characteristics, andoften alternatives for correcting deficiencies withoutincurring expense and delay of building hardware.

Monte Carlo reliability models can realistically assessplant conditions when combined with costs, repairtimes, and statistical events. Monte Carlo simulationmodels are very helpful for considering approximateoperating conditions in a plant including cost effectivesizing of tankage to provide protection for shortduration equipment failures.

Good simulation models help determine maintenancestrategies and turnaround timing for equipmentrenewal as simulation models are usually based onsimple, heuristic rules. Heuristic rules are based onobserved behavior of components or systems andheuristic rules are easy to construct using computersystems knowledge based software.

Reliability models stimulate creative ideas for solvingcostly problems and prevents replication of oldproblems. Reliability models offer a scientificmethod for studying actions, responses, and costs inthe virtual laboratory of the computer using actualfailure data from existing plants. Monte Carlomodels are never better than the data supplied.

Monte Carlo models provide a way to search forlowest cost operating alternatives and conditions bypredicting the outcome of conditions, events, andequipment. Monte Carlo models aid in finding thelowest long term cost of ownership.

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Other reliability tools-The list of reliability tools isgreat. (Barringer 1996b) The real key to success isputting reliability tools, developed in the past 60years, to work for cost reductions. Few engineersreceive university level training in the use ofreliability tools for solving practical problems. Thenew reliability engineering tools require educatingmanagement and training engineers in their use toreduce risk, reduce costs, and improve operations.

SummaryUse failure data from existing plants with new toolsto solve reliability problems in practical ways.Businesses cannot afford too little nor too muchreliability—reliability must be harmonized with costissues.

Know the cost of unreliability based on failure data.It is an important reliability index. The cost ofunreliability must be engineered and controlled bysolving top level items on the Pareto list.

As expertise in using failure data grows, use Weibullanalysis to help with problem solving by puttingstatistics to work on practical problems. Weibullresults give details for supporting RCM activities.

Train staffs in use of new reliability tools to gain acompetitive business advantage by increasing skills toreduce costs. Cutting edge companies are using, orconsidering, new tools such as Monte Carlo modelsfor finding cost effectively alternatives to reducetheir cost of unreliability.

ReferencesAbernethy, Dr. Robert B., 1996, The New WeibullHandbook, 2nd edition, Self published, North PalmBeach, FL, Phone: 407-842-4082.

Barringer, H. Paul, 1993, Reliability EngineeringPrinciples, Self published, Humble, TX, Phone: 713-852-6810.

Barringer, H. Paul, and Weber, David P., 1995,“Where Is My Data For Making ReliabilityImprovements”, Hydrocarbons Processing

Magazine 4th International ReliabilityConference , Houston, TX.

Barringer, H. Paul, 1996, temporary web address athttp://www.forbes.net//barring and look for apermanent world wide web site address athttp://www.barringer1.com

Barringer, H. Paul, 1996, “An Overview OfReliability Engineering Principles”, Energy Week1996 Conference, PennWell Conferences &Exhibitions , Book IV, Houston, TX.

Center For Process Safety, 1989, Guidelines ForProcess Equipment Reliability Data, AmericanInstitute of Chemical Engineers, New York.

Fulton, Wes, 1996, WinSMITH™ Weibull version1.0J for Windows 3.1, Windows 95 and WindowsNT, Fulton Findings, Torrance, CA, Phone: 310-548-6358.

Moubray, John, 1992, Reliability-centeredMaintenance, Industrial Press, New York

IEEE Std 500-1984: IEEE Guide to the Collectionand Presentation of Electrical, Electronic,Sensing Component, and MechanicalEquipment Reliability Data for Nuclear-PowerGenerating Stations , John Wiley & Sons, Inc.,New York.

Omdahl, Tracy Philip, 1988 Reliability,Availability, and Maintainability (RAM)Dictionary, ASQC Quality Press, Milwaukee, WI,Phone: 1-800-248-1946

OREDA-1992 (Offshore Reliability Data),Contact Andy Wolford at DNV Technica, 16340Park Ten Place, Suite 100, Houston, TX 77084,phone 713-647-4225, or FAX 713-647-2858.

RAC-NPRD-95, 1994 Nonelectronic PartsReliability Data 1995, Reliability Analysis Center,PO Box 4700, Rome, NY 13442-4700, Phone: (800)-526-4802

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May 5, 1996