Reliability analysis of load-sharing man-machine systems subject to machine degradation, human errors, and random shocks

Haiyang Che, Shengkui Zeng, Kehui Li and Jianbin Guo

Reliability Engineering and System Safety, 2022, vol. 226, issue C

Abstract: This paper considers a load-sharing man-machine system with multiple man-machine units (MMUs) subject to machine degradation, human errors, and random shocks. Human errors affect the machine that the person operates in a MMU and contribute to dependent competing failure processes (DCFPs) of unit (DCFPsU): soft and hard failure processes. Random shocks affect all MMUs and contribute to DCFPs of system (DCFPsS): unit soft/hard failure one after another until the system failure and common cause failures. In addition, once a MMU fails, more workloads are shared on surviving MMUs due to load-sharing characteristic, which increase not only machine degradation rates but also human error intensities. We incorporate human errors into reliability analysis besides degradation processes and random shocks and develop an analytical reliability model of load-sharing man-machine system. A Poisson-type human error model considering the effects of workload is proposed first. To model the DCFPsU, DCFPsS, and load-sharing characteristic, conditional distributions of DCFPsU and conditional probability density functions of MMU failures are investigated. A system reliability model is then developed based on total probability formula through analytical derivation. Finally, a load-sharing 2-out-of-3:G lathe system and small-, medium- and large-scale examples are implemented to illustrate the effectiveness of the reliability model.

Keywords: Reliability modeling; Load-sharing man-machine system; Dependent competing failure processes; Machine degradation; Human error; Random shocks (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (15)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:226:y:2022:i:c:s095183202200312x

DOI: 10.1016/j.ress.2022.108679

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