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Set theoretic formulation of performance reliability of multiple response time‐variant systems due to degradations in system components
Author(s) -
Son Young Kap,
Savage Gordon J.
Publication year - 2007
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.783
Subject(s) - reliability (semiconductor) , reliability engineering , warranty , monte carlo method , component (thermodynamics) , computer science , quality (philosophy) , set (abstract data type) , degradation (telecommunications) , limit (mathematics) , importance sampling , function (biology) , mathematical optimization , power (physics) , engineering , mathematics , statistics , telecommunications , mathematical analysis , physics , philosophy , quantum mechanics , evolutionary biology , biology , political science , law , thermodynamics , programming language , epistemology
This paper presents a design stage method for assessing performance reliability of systems with multiple time‐variant responses due to component degradation. Herein the system component degradation profiles over time are assumed to be known and the degradation of the system is related to component degradation using mechanistic models. Selected performance measures (e.g. responses) are related to their critical levels by time‐dependent limit‐state functions. System failure is defined as the non‐conformance of any response and unions of the multiple failure regions are required. For discrete time, set theory establishes the minimum union size needed to identify a true incremental failure region. A cumulative failure distribution function is built by summing incremental failure probabilities. A practical implementation of the theory can be manifest by approximating the probability of the unions by second‐order bounds. Further, for numerical efficiency probabilities are evaluated by first‐order reliability methods (FORM). The presented method is quite different from Monte Carlo sampling methods. The proposed method can be used to assess mean and tolerance design through simultaneous evaluation of quality and performance reliability. The work herein sets the foundation for an optimization method to control both quality and performance reliability and thus, for example, estimate warranty costs and product recall. An example from power engineering shows the details of the proposed method and the potential of the approach. Copyright © 2006 John Wiley & Sons, Ltd.