Component Importance Measures for Components With Multiple Dependent Competing Degradation Processes and Subject to Maintenance

• Published in: IEEE transactions on reliability Vol. 65; no. 2; pp. 547 - 557
• Main Authors: Yan-Hui Lin, Yan-Fu Li, Zio, Enrico
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Allocations; Degradation; Degradation dependency; Deviation; Engineering Sciences; finite-volume approach; Heating; importance measures; Inspection; Maintenance; Maintenance engineering; Maintenance management; Mathematical model; Mathematical models; multiple dependent competing degradation processes; nuclear power plant; Nuclear power plants; piecewise-deterministic Markov process (PDMP); Preventive maintenance; Reliability; residual heat removal system; System performance; Tasks
• DOI: 10.1109/TR.2015.2500684
• ISSN: 0018-9529

Component importance measures (IMs) are widely used to rank the importance of different components within a system and guide allocation of resources. The criticality of a component may vary over time, under the influence of multiple dependent competing degradation processes and maintenance tasks. Neglecting this may lead to inaccurate estimation of the component IMs and inefficient related decisions (e.g., maintenance, replacement, etc.). The work presented in this paper addresses the issue by extending the mean absolute deviation IM by taking into account: 1) the dependency of multiple degradation processes within one component and among different components; 2) discrete and continuous degradation processes; and 3) two types of maintenance tasks: condition-based preventive maintenance via periodic inspections and corrective maintenance. Piecewise-deterministic Markov processes are employed to describe the stochastic process of degradation of the component under these factors. A method for the quantification of the component IM is developed based on the finite-volume approach. A case study on one section of the residual heat removal system of a nuclear power plant is considered as an example for numerical quantification.