Life Data Analysis Flashcards
7 cards from real CRE practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 7 Life Data Analysis flashcards as text
The inverse power law model in accelerated life testing relates life to stress S by the equation L(S) = A/S^n. This model is most commonly applied to:
Answer: Non-thermal stresses such as voltage or mechanical load
The inverse power law is the standard model for non-thermal accelerating stresses like voltage, pressure, or mechanical load.
When plotting Weibull data, the Y-axis value for each failure is derived from:
Answer: A probability estimate (such as the median rank) for that failure
The Y-axis on a Weibull plot represents the estimated cumulative failure probability (unreliability) F(t) for each ordered failure, typically using median ranks.
Likelihood ratio confidence bounds in life data analysis are generally preferred over Fisher matrix bounds because they:
Answer: Are more accurate for small samples and highly censored data
Likelihood ratio bounds do not rely on asymptotic normality and therefore perform better for small, censored, or skewed datasets.
In reliability growth analysis (Duane/AMSAA model), if the cumulative failure rate is plotted vs. cumulative test time on log-log paper, a straight line slope indicates:
Answer: Constant reliability growth rate
A straight line on a Duane plot (log cumulative failures vs. log cumulative time) indicates a consistent, steady reliability growth rate.
For a 2-parameter lognormal distribution fit to life data, the parameter μ' (log-mean) corresponds to:
Answer: The natural log of the median failure time
In lognormal analysis, μ' is the mean of the log-transformed times, which equals the natural log of the median (50th percentile) failure time.
A component has a Weibull shape parameter β = 3.5. This suggests the component's failure behavior is dominated by:
Answer: Wear-out or aging mechanisms
A shape parameter β > 1 (especially β ≈ 3–4) indicates an increasing failure rate characteristic of wear-out, fatigue, or aging degradation.
When using the Nelson-Aalen estimator for life data, the quantity estimated at each failure time is the:
Answer: Cumulative hazard function H(t)
The Nelson-Aalen estimator is a non-parametric estimator of the cumulative hazard function H(t) = −ln[R(t)].