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Forecasting Future Failures From Your Maintenance Database

Paul Barringer, PEBarringer & Associates, Inc.Humble, TX 77347-3985Phone: 1-281-852-6810Email: hpaul@barringer1.comWeb: http://www.barringer1.com

Abstract• Failure data in your maintenance database can be used to predict

future failures using reliability growth plots with straight lines in log-log formats of cum failures (Y-axis) and cum time (X-axis).

• When you have made improvements, the reliability growth plots will show a cusp in the line when failure reductions occur.

• If the plots are made in money vs time, you can quantify the savings of your improvements.

• Here’s the reliability growth plot challenge for you since most people don’t believe such a simple tool can forecast future failures:

• If I can predict your future failures, why can’t you prevent them to save money and make my fearless forecast of future failures wrong by preventing the failures?

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Duane To Crow-AMSAA Plots• In 1967, James Duane worked for GE in their

reliability test lab. He plotted cum failures vs cum test time on log-log plots he found straight lines with line slope ≈ 0.5 (indicating product improvements from the development process).

• In the early ‘80’s Larry Crow worked for AMSAA*. He wondered why log-log plots of cum failures vscum time made straight lines. He produced Crow’s math proof in MIL-HDBK-189**.

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* US Army Material System Analysis Activity** Download: barringer1.com/mil_files/MIL-HDBK-189C.pdf

Why Crow-AMSAA

• Duane said most of his reliability plots has line slopes of ~0.5 (for GE’s improvements)

• Larry Crow’s plots of AMSAA data had all sorts of line slopes. Larry said if the line slope <1 reliability improves, if ≈ 1 no change, if >1 reliability deteriorates.

• The straight line plots allow easy prediction of future failures with straight trend lines.

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Actual Failure Data-Chem Plant

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Actual failure data from a chemical plant in Brazil. Starting in October ’95 improvements were initiated and failures began to decline! This says we should expect to find a cusp on the trend line.

The total productive maintenance (TPM) program encouraged operator to take ownership for their equipment with coaching from the consultants hired to initiate improvements.

In short, the following Crow-AMSAA plot will be a “show me—don’t tell me” about the improvements.

Crow-AMSAA Intervention Plot

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Very Slight ImprovementBefore TPM, < 1

Big ImprovementAfter TPM, << 1

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4 Engineering FoundMost Pumps Off BEPAnd Set Priority List

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Interventions Fearless Forecast

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The future forecast isavailable from the Crow-AMSAA plots

Failures (maintenance interventions) are now~1/5 of previousyears.

Time to hold the progress and get new projects underway.

Saved $1 Million Since July ’02?

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July ‘02Not monotonically increasing include in data set but do not plot

Heavy Mobil Equipment

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First Look At Maintenance Costs

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Long term, costs are increasing!

Looks like a cuspat ~1100+ days.

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Saved $1 Million Since July ’02?

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$ = 110839(1461)^1.192 = $17,672,140

= $2,222,000 -$1,074,000 = $1,148,000 savings from July ‘02 TPM

announcement to end of 2002 whichis ~1/2 of the savings since Feb ’02

$ = 3361(1461)^1.192 = $19,894,433

= $2,222,000 savings since Feb ‘02

$ = 3361(1308)^1.192 = $17,436,726

$ = 110839(1308)^1.192 = $16,362,573

= $1,074,000 savings in July ‘02since Feb ‘02

The key question was why the measure from July?---the answer was that’s when they got their walk around PDAs for the operators!

Please note the program received no cost reductions from issuance of PDAs.

The program was on the correct course via solicitation of help from the operators acting as owners of the equipment rather than renters of the equipment.

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Safety Incidents Canadian Plant

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Safety Incidents Are Failures!

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Unfavorable TrendsActual

Forecast

Show me, don’t tell me,your safety record is this good!

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Plant Spill Data

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Table 5

Spill DateDays

Between Spill

Spill Events

Cum. Days

Cum. Spills

Failures Predicted

By Old Method

New Method Savings

11/18/1995 35 1 35 11/31/1996 74 1 109 2

5/8/1996 98 2 207 45/22/1996 14 1 221 57/29/1996 68 1 289 68/23/1996 25 1 314 78/25/1996 2 1 316 86/20/1997 299 1 615 9 18 92/22/1998 247 1 862 10 27 172/10/1999 353 1 1215 11 41 30

Raw Data Crow/AMSAA Data Forecasts

8 failures In316 daysThis is the sad datum!

3 failures In 899 days—this is good news!

Crow-AMSAA Spill Data

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Spill Data A Little Later

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Later Crow-AMSAA Plot

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We’veLostTheWay ToSuccess!

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Co-Gen Facility As An Island

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Co-genFailure Log:31 ForcedOutages In1432 Days= 46.5 days per failure

Co-Gen Crow-AMSAA Plot

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No Change

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Fearless Failure Forecast

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This failure wasdramatic! Whilethe reliability engineer wasexplaining to the utility group thata failure was imminent, basedon Crow-AMSAAforecast, theco-gen systemtripped and thelights went out!

5.5 Years Of Repair Orders

• Plant sites have been stable. No big expansions, acquisitions, or closures

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Corporate View Of Repair Data

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Repair-focused for 5-1/2 years across the corporation as > 1 for repairorders excludingPMs!

MTBF Transform Adds Info

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Mean time between repair orders has recently stabilized at ~53 orders/day

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72” Cooling Water Line

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Repair-focused with = 2.5 changed to reliability-focused with = 0.34!

Each failure exceeds$1,000,000!

Middle East Plant Safety

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Injury accepting with = 12.3 changed to injury prevention with = 0.61

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Middle East Safety Transform

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Mean time between safety event is growing from a reliability-focus

Summary

• Eight actual datasets show straight lines segments of cum failures vs cum time

• Crow-AMSAA straight line plots allows failure forecast of mixed failure modes

• Forecast failures, then start decisive action to prevent predicted failures

• Preventing failures requires proactive effort

• Preventing future failures saves $!

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