Estimating component-defect probability from masked system success/failure data

Summary & Conclusions - Consider a system of k components that fails whenever there is a defect in at least one of the components. Due to cost & time constraints it is not feasible to learn exactly which components are defective. Instead, test procedures ascertain that the defective components belong to some subset of the k components. This phenomenon is termed masking. We describe a 2-stage procedure in which a sample of masked subsets is subjected to intensive failure analysis. This enables maximum-likelihood estimation of the defect probability of each individual component and leads to diagnosis of the defective components in future masked failures. © 1996 IEEE.