Reliability Extending the Quality Concept

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

Summary Slide 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

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

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

Probability distributions Exponential  “Random failure” Log-normal Weibull Gamma

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

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

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

Shapes of Weibull

Scale of Weibull

Location of Weibull

Gamma distribution

Non-parametric data fit

Summary Slide 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

Accelerated life testing

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

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

Multi-environment overstress 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

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

Step-stress Cumulative damage model Harder to relate to reality

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

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

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

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

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

Effects of design Usually the heart of warranty issues Counteract with robust design

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

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

Warranty 1-dimensional  Example: miles only 2-dimensional  Example: Miles Years

Warranty Non-renewing Pro-rated Cumulative  Multiple items Reliability improvement

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

Parallel reliability (redundancy) Dramatically reduces probability of failure

Other tools FMEA Fault Tree Analysis Reliability Block Diagrams  Simulation

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

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

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

Pogo Pins

Pogo Pins (product 1)

Pogo Pins (Product 2)

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

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

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