Predictive analytics: Assessing failure rate accuracy & failure mode completeness

This paper introduces a benchmarking technique we call predictive analytics (PA). The benchmark for the constant failure rate (λ) of a specific failure mode of an element (e.g., pressure transmitter, microprocessor, valve, etc.) used to implement a safety instrumented function (SIF) is predicted using the failure modes, effects and diagnostic analysis (FMEDA) technique supported by a database of constant failure rates and failure mode distributions for the components which comprise the element. This benchmark represents the λ of that failure mode inherent in the element during its useful life. The λ for the same failure mode of the element is estimated from field failure data (FFD) and compared to the benchmark. It is not uncommon for the benchmark λ and estimated λ to differ considerably. PA provides a procedure for exploring explanations of these differences and assessing the accuracy of the estimated element λ with respect to the benchmark λ of the element. PA can often determine the source of that portion of the estimated λ value not inherent to the element but likely due to random failures of infant mortality, wear out, or initial failures, to systematic failures, or to application or site specific issues. This site specific element λ is the portion of the estimated λ the end user needs to address to improve operational reliability and safety. PA can also assess the quality of FFD and can facilitate the discovery of previously unknown element failure modes.