Time space modelling for fault diagnosis and prognosis with uncertainty management: A general theoretical formulation

Dongzhen Lyu, Guangxing Niu, Enhui Liu, Bin Zhang, Gang Chen, Tao Yang and Enrico Zio

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

Abstract: This paper provides a general theoretical framework of time space modelling for Lebesgue sampling-based fault diagnosis and prognosis, with the detailed theoretical analysis and experimental simulation for its mathematical modelling and uncertainty quantification. The developed framework introduces the concept of sequential estimation and prediction of "Mean Reach Time" for fault degrading to predefined Lebesgue states. The proposed formulation comprehensively shows the robustness and generalization of the Time Space Model. It also demonstrates its effectiveness in reducing model complexity and elaborates its easy integration with commonly used techniques. Two applications are presented regarding batteries capacity degradation and bearings degradation. The results demonstrate the compatibility, effectiveness, and modelling simplicity of the proposed formulation.

Keywords: Time space modelling; Prognosis and health management; Uncertainty management; Lebesgue sampling; Mean Reach Time (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:226:y:2022:i:c:s0951832022003192

DOI: 10.1016/j.ress.2022.108686

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