Reliability Testing and Data Analysis
Test types
Know the purpose of each: reliability demonstration (verify a requirement), reliability growth (improve), accelerated life testing (compress time via stress), environmental testing (expose to conditions), and screening/burn-in (remove early failures). Traps: using a demonstration test to claim growth, or an accelerated test without validating the acceleration model.
Sampling and confidence
Demonstration tests use success-run or time-terminated plans. For a zero-failure success-run test, R = (1 - C)^(1/n), where C is confidence and n is sample size. For time-terminated tests, use chi-square relationships: for zero failures, MTBF lower confidence limit = 2T / chi-square(2r+2, confidence) with r = 0. Know that confidence and sample size trade off.
Data analysis
Fit distributions to failure data; use probability plots to check fit. Censored data (units still running) must be included; ignoring suspensions biases results. Watch for mixing failure modes, which distorts a single-distribution fit.
Accelerated testing
Arrhenius (temperature), inverse power law (voltage, stress), and Coffin-Manson (thermal cycling) are common models. Extrapolation beyond tested stress ranges is risky.
Worked example
Zero-failure demonstration with 90% confidence and n = 22: R = (1 - 0.90)^(1/22) = 0.10^(0.0455) = about 0.90. So 22 units with no failures demonstrate roughly 90% reliability at 90% confidence.