Physics-embedding multi-response regressor for time-variant system reliability assessment

Lu-Kai Song, Fei Tao, Xue-Qin Li, Le-Chang Yang, Yu-Peng Wei and Michael Beer

Reliability Engineering and System Safety, 2025, vol. 263, issue C

Abstract: Efficient time-variant reliability assessment for complex systems is of great interest but challenging as the highly complex multiple output responses under time-variant uncertainties are hard to quantify. The task becomes even more challenging if the interconnected dependencies between multiple failure modes are involved. In this study, an eXtreme physics-embedding multi-response regressor (X-PMR) is presented for time-variant system reliability assessment. Firstly, by transforming time-variant multiple responses to time-invariant extreme values, an eXtreme multi-domain transformation concept is presented, to establish the time-invariant multi-input multi-output (TiMIMO) dataset; moreover, by embedding physics/mathematics knowledge into multi-objective ensemble modeling, a physics-embedding multi-response regressor is proposed, to synchronously construct the surrogate model for highly complex multiple output responses. The validation effectiveness and benefit illustration of the X-PMR method are revealed by introducing three numerical systems (i.e., series system, parallel system and series/parallel hybrid system) and a real application system (i.e., dynamic aeroengine turbine blisk), in comparison with a number of state-of-the-art methods investigated in the literature. The current efforts can provide a novel sight to address the time-variant system reliability assessment problems.

Keywords: Time-variant reliability; System reliability; Surrogate model; Aeroengine; Turbine blisk (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:263:y:2025:i:c:s0951832025004636

DOI: 10.1016/j.ress.2025.111262

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