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Reliability

Reliability. Extending the Quality Concept. ASQ CQA CQE CSSBB CRE APICS CPIM. Director of Product Integrity & Reliability for Stoneridge TED Background in metallurgy & materials science. Kim Pries. What is reliability? Reliability data Probability distributions

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Reliability

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  1. Reliability Extending the Quality Concept

  2. ASQ CQA CQE CSSBB CRE APICS CPIM Director of Product Integrity & Reliability for Stoneridge TED Background in metallurgy & materials science Kim Pries

  3. What is reliability? Reliability data Probability distributions Most common distribution Weibull mean Citation Shapes of Weibull Scale of Weibull Location of Weibull Gamma distribution Non-parametric data fit Summary Slide

  4. What is reliability? • Reliability is the “quality concept” applied over time • Reliability engineering requires a different tool box

  5. Reliability data • Nearly always “units X to failure,” where units are most often • Miles • Hours (days, weeks, months)

  6. Probability distributions • Exponential • “Random failure” • Log-normal • Weibull • Gamma

  7. Most common distribution Equation • Weibull distribution eta = scale parameter, beta = shape parameter (or slope), gamma = location parameter.

  8. Weibull mean • Also known as MTBF or MTTF • Need to understand gamma function

  9. Citation • Using diagrams from Reliasoft Weibull++ 7.x • A few from Minitab

  10. Shapes of Weibull

  11. Scale of Weibull

  12. Location of Weibull

  13. Gamma distribution

  14. Non-parametric data fit

  15. Accelerated life testing Accelerated Life Testing Highly accelerated life testing Multi-environment overstress MEOST, continued Step-stress HASS and HASA Achieving reliability growth Reliability Growth-Duane Model Reliability Growth-AMSAA model Summary Slide

  16. Accelerated life testing

  17. Accelerated Life Testing • Can be used to predict life based on testing • A typical model looks like

  18. Highly accelerated life testing • No predictive value • Reveals weakest portions of design • Examples: • Thermal shock • Special drop testing • Mechanical shock • Swept sine vibration

  19. Derate components Study thermal behavior Scan Finite element analysis Modular designs DFM Mfg line ‘escapes’ RMAs Robust…high S/N ratio Design for maintainability Product liability analysis Take apart supplier products FFRs Multi-environment overstress

  20. MEOST, continued • Test to failure is goal • Combined stress environment • Beyond design levels • Lower than immediate destruct level • Example: • Simultaneous • Temperature • Humidity • Vibration

  21. Step-stress • Cumulative damage model • Harder to relate to reality

  22. HASS and HASA • Screening versus sampling • Small % of life to product • Elicit ‘infant mortality’ failures • Example: • Burn-in

  23. Achieving reliability growth • Detect failure causes • Feedback • Redesign • Improved fabrication • Verification of redesign

  24. Cruder than AMSAA model Shows same general improvement Reliability Growth-Duane Model

  25. Cumulative failures Initially very poor Improves over time Reliability Growth-AMSAA model

  26. Effects of design Effects of manufacturing Can’t we predict? Warranty Warranty Serial reliability Parallel reliability (redundancy) Other tools Software reliability Summary Slide

  27. Effects of design • Usually the heart of warranty issues • Counteract with robust design

  28. Effects of manufacturing • Manufacturing can degrade reliability • Cannot improve intrinsic design issues

  29. Can’t we predict? • MIL-HDBK-217F • No parallel circuits • Electronics only • Extremely conservative • Leads to over-engineering • Excessive derating • Off by factors of at least 2 to 4

  30. Warranty • 1-dimensional • Example: miles only • 2-dimensional • Example: • Miles • Years

  31. Warranty • Non-renewing • Pro-rated • Cumulative • Multiple items • Reliability improvement

  32. Serial reliability • Simple product of the probabilities of failure of components • More components = less reliability

  33. Parallel reliability (redundancy) • Dramatically reduces probability of failure

  34. Other tools • FMEA • Fault Tree Analysis • Reliability Block Diagrams • Simulation

  35. Software reliability • Difficult to prove • Super methods • B-method • ITU Z.100, Z.105, and Z.120 • Clean room

  36. Summary Slide • What about maintenance? • Pogo Pins • Pogo Pins (product 1) • Pogo Pins (Product 2) • Pogo Pin conclusions • Preventive vs. Predictive

  37. What about maintenance? • Same math • Looking for types of wear and other failure modes

  38. Pogo Pins

  39. Pogo Pins (product 1)

  40. Pogo Pins (Product 2)

  41. Pogo Pin conclusions • Very quick “infant mortality” • Random failure thereafter • Difficult to find a nice preventive maintenance schedule • Frequent inspection

  42. Preventive vs. Predictive • Preventive maintenance • Fix before it breaks • Statistically based intervals • Predictive maintenance • Detect anomalies • Always uses sensors

  43. The future • Combinatorial testing • Designed experiments • Response surfaces • Analysis of variance • Analysis of covariance • Eyring models • Multiple environments

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