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

Benjamin Reiser, Betty J. Flehinger, Andrew R. Conn

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)238-243
Number of pages6
JournalIEEE Transactions on Reliability
Issue number2
StatePublished - 1996
Externally publishedYes

Bibliographical note

Funding Information:
We are grateful for the thorough review & editing of Dr. R. A. Evans. We thank Peggy Cargiulo for her manuscript preparation. Benjamin Reiser was a visiting scientist on leave from the University of Haifa. Andrew R. Conn’s research was supported in part by the Advanced Research Projects Agency of the Department of Defense and was monitored by the US Air Force Office of Scientific Research under Contract No. F49620-9 1- C-0079. The US Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright hereon.


  • Defect probability
  • Diagnostic probability
  • Masking
  • System component

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Electrical and Electronic Engineering


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