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

Consider a system of k components that fails whenever there is a defect in at least one of the components. Due to cost and 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. The authors 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