Monitoring reliability of a system with random failure threshold under extreme shocks

Shamstabar, Yousof; Ebrahimmagham, Neda; Shahriari, Hamid

doi:10.1016/j.ress.2024.109973

Reliability Engineering & System Safety

2024

DOI: 10.1016/j.ress.2024.109973

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Monitoring reliability of a system with random failure threshold under extreme shocks

Yousof Shamstabar,

Neda Ebrahimmagham,

Hamid Shahriari

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Cited by 5 publications

References 28 publications

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Reliability modeling of systems with random failure threshold subjected to cumulative shocks using machine learning method

Shamstabar,

Ebrahimmagham,

Samimi

et al. 2024

Quality & Reliability Eng

Self Cite

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Modeling and evaluation of a system reliability is a key issue in shock model analysis. In cumulative shock model, a system fails when the total damages caused by shocks exceeds the system failure threshold level. The reliability of a system with random failure threshold level under cumulative shocks is investigated in this work, considering the general dependency between inter‐arrival times of shocks and their damages magnitudes. Clustering, as one of the unsupervised machine learning methods, is utilized here by applying a genetic algorithm (GA) and particle swarm optimization algorithm (PSO) for its implementation. Phase‐type (PH) distributions and their properties are also applied for finding a system failure distribution functions, determining the convolutions, evaluating the integrals, and ultimately modeling the system reliability. An illustrative example is provided to demonstrate the implementation and effectiveness of the proposed modeling procedure and the suggested approach for computing the system reliability and remaining useful lifetime (RUL). Also, an example case study of X‐ray machine in healthcare industry is provided to demonstrate the application of the proposed model. Applying other machine learning methods for patterns recognition is an area for future research.

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Reliability modeling of systems with random failure threshold subjected to cumulative shocks using machine learning method

Shamstabar,

Ebrahimmagham,

Samimi

et al. 2024

Quality & Reliability Eng

Self Cite

View full text Add to dashboard Cite

show abstract

Task-orientated probabilistic damage model with interdependent degradation behaviors for RUL prediction of traction converter systems

Liao,

Peng,

et al. 2024

Reliability Engineering & System Safety

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Corrective maintenance minimizing expected mission losses in shock exposed multistate production-storage systems

Levitin,

Xing,

Dai

2024

Reliability Engineering & System Safety

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Monitoring reliability of a system with random failure threshold under extreme shocks

Cited by 5 publications

References 28 publications

Reliability modeling of systems with random failure threshold subjected to cumulative shocks using machine learning method

Reliability modeling of systems with random failure threshold subjected to cumulative shocks using machine learning method

Task-orientated probabilistic damage model with interdependent degradation behaviors for RUL prediction of traction converter systems

Corrective maintenance minimizing expected mission losses in shock exposed multistate production-storage systems

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