Forecasting Future Failures From Your Maintenance Database

Transcription

1 Forecasting Future Failures From Your Maintenance Database Paul Barringer, PE Barringer & Associates, Inc. Humble, TX Phone: Web: Abstract Failure data in your maintenance database can be used to predict future failures using reliability growth plots with straight lines in loglog 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? 2 1

2 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 vs cum time made straight lines. He produced Crow s math proof in MIL-HDBK-189**. * US Army Material System Analysis Activity ** Download: barringer1.com/mil_files/mil-hdbk-189c.pdf 3 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. 4 2

3 Actual Failure Data-Chem Plant 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. 5 Crow-AMSAA Intervention Plot Engineering Found Most Pumps Off BEP And Set Priority List Very Slight Improvement Before TPM, < 1 Big Improvement After TPM, << 1 6 3

4 Interventions Fearless Forecast The future forecast is available from the Crow-AMSAA plots Failures (maintenance interventions) are now ~1/5 of previous years. Time to hold the progress and get new projects underway. 7 Saved $1 Million Since July 02? Heavy Mobil Equipment Not monotonically increasing include in data set but do not plot July

5 First Look At Maintenance Costs 1 Looks like a cusp at ~ days. Long term, costs are increasing! 9 Saved $1 Million Since July 02? 4 $ = 3361(1461)^1.192 = $19,894,433 $ = (1461)^1.192 = $17,672,140 = $2,222,000 savings since Feb $ = 3361(1308)^1.192 = $17,436,726 $ = (1308)^1.192 = $16,362,573 = $1,074,000 savings in July 02 since Feb 02 1 = $2,222,000 -$1,074,000 = $1,148,000 savings from July 02 TPM announcement to end of 2002 which is ~1/2 of the savings since 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. 10 5

6 Safety Incidents Canadian Plant 11 Safety Incidents Are Failures! Unfavorable Trends Actual Forecast Show me, don t tell me, your safety record is this good! 12 6

7 Table 5 Spill Date Plant Spill Data Raw Data Crow/AMSAA Data Forecasts Days Between Spill Spill Events Cum. Days Cum. Spills 11/18/ /31/ /8/ /22/ /29/ /23/ /25/ Failures Predicted By Old Method New Method Savings 8 failures In 316 days This is the sad datum! 6/20/ /22/ /10/ failures In 899 days this is good news! 13 Crow-AMSAA Spill Data 14 7

8 Spill Data A Little Later 15 Later Crow-AMSAA Plot We ve Lost The Way To Success! 16 8

9 Co-Gen Facility As An Island Co-gen Failure Log: 31 Forced Outages In 1432 Days = 46.5 days per failure 17 Co-Gen Crow-AMSAA Plot No Change 18 9

10 Fearless Failure Forecast This failure was dramatic! While the reliability engineer was explaining to the utility group that a failure was imminent, based on Crow-AMSAA forecast, the co-gen system tripped and the lights went out! Years Of Repair Orders Plant sites have been stable. No big expansions, acquisitions, or closures 20 10

11 Corporate View Of Repair Data Repair-focused for 5-1/2 years across the corporation as > 1 for repair orders excluding PMs! 21 MTBF Transform Adds Info Mean time between repair orders has recently stabilized at ~53 orders/day 22 11

12 72 Cooling Water Line Repair-focused with = 2.5 changed to reliability-focused with = 0.34! Each failure exceeds $1,000,000! 23 Middle East Plant Safety Injury accepting with = 12.3 changed to injury prevention with =

13 Middle East Safety Transform Mean time between safety event is growing from a reliability-focus 25 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 $! 26 13