This paper estimates defect and outbreak probabilities of each individual component from some subset of masked data where the exact component causing system failure might be unknown. A system consists 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. Because, test procedures ascertain that the defective components belong to some subset of the k components. This phenomenon is termed masking. We describe a, b, c type in which a sample of masked subsets is subjected to intensive failure analysis. This recorded data of a, b, c type enables maximum likelihood estimation of defect probability of each individual component and leads to outbreak of the defective components in future masked failures.