TY - JOUR

T1 - Extending the survival signature paradigm to complex systems with non-repairable dependent failures

AU - George-Williams, Hindolo

AU - Feng, Geng

AU - Coolen, Frank PA

AU - Beer, Michael

AU - Patelli, Edoardo

N1 - Funding information: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was partially supported by the EPSRC grant Smart on-line monitoring for nuclear power plants (SMART) (EP/M018415/1) and the EPSRC and ESRC Centre for Doctoral Training on Quantification and Management of Risk & Uncertainty in Complex Systems & Environments (EP/ L015927/1).

PY - 2019/8

Y1 - 2019/8

N2 - Dependent failures impose severe consequences on a complex system’s reliability and overall performance, and a realistic assessment, therefore, requires an adequate consideration of these failures. System survival signature opens up a new and efficient way to compute a system’s reliability, given its ability to segregate the structural from the probabilistic attributes of the system. Consequently, it outperforms the well-known system reliability evaluation techniques, when solicited for problems like maintenance optimisation, requiring repetitive system evaluations. The survival signature, however, is premised on the statistical independence between component failure times and, more generally, on the theory of weak exchangeability, for dependent component failures. The assumption of independence is flawed for most realistic engineering systems while the latter entails the painstaking and sometimes impossible task of deriving the joint survival function of the system components. This article, therefore, proposes a novel, generally applicable, and efficient Monte Carlo Simulation approach that allows the survival signature to be intuitively used for the reliability evaluation of systems susceptible to induced failures. Multiple component failure modes, as well, are considered, and sensitivities are analysed to identify the most critical common-cause group to the survivability of the system. Examples demonstrate the superiority of the approach.

AB - Dependent failures impose severe consequences on a complex system’s reliability and overall performance, and a realistic assessment, therefore, requires an adequate consideration of these failures. System survival signature opens up a new and efficient way to compute a system’s reliability, given its ability to segregate the structural from the probabilistic attributes of the system. Consequently, it outperforms the well-known system reliability evaluation techniques, when solicited for problems like maintenance optimisation, requiring repetitive system evaluations. The survival signature, however, is premised on the statistical independence between component failure times and, more generally, on the theory of weak exchangeability, for dependent component failures. The assumption of independence is flawed for most realistic engineering systems while the latter entails the painstaking and sometimes impossible task of deriving the joint survival function of the system components. This article, therefore, proposes a novel, generally applicable, and efficient Monte Carlo Simulation approach that allows the survival signature to be intuitively used for the reliability evaluation of systems susceptible to induced failures. Multiple component failure modes, as well, are considered, and sensitivities are analysed to identify the most critical common-cause group to the survivability of the system. Examples demonstrate the superiority of the approach.

KW - Dependencies

KW - Monte Carlo simulation

KW - multiple failure mode

KW - survival signature

KW - system reliability

UR - http://www.scopus.com/inward/record.url?scp=85059952891&partnerID=8YFLogxK

U2 - 10.1177/1748006X18808085

DO - 10.1177/1748006X18808085

M3 - Article

AN - SCOPUS:85059952891

VL - 233

SP - 505

EP - 519

JO - Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability

JF - Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability

SN - 1748-006X

IS - 4

ER -