Reliability analysis of systems considering clusters of dependent degrading components

Yousefi, Nooshin; Coit, David W.; Song, Shousen

doi:10.1016/j.ress.2020.107005

Cited by 39 publications

(13 citation statements)

References 43 publications

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“…Extensive studies have been undertaken on the reliability of dependent competing failure processes (DCFPs). [35][36][37][38][39][40] For example, Che et al 41 proposed a simplified model that uses Markov point processes to model shock processes. In their work, shocks contribute to an abrupt increase in degradation, and the intensity of the onset of the shocks varies once the degradation has accumulated.…”

Section: Introductionmentioning

confidence: 99%

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Optimal condition‐based maintenance policy for systems with mutually dependent competing failure

Huang

2023

Quality & Reliability Eng

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In this paper, a novel condition‐based maintenance (CBM) policy for systems with mutually dependent competing failure processes (DCFPs) is studied. The cumulative random shocks contribute to the increased rate of degradation, meanwhile, the hard failure threshold is reduced when the amount of degradation reaches a threshold. The degradation process is simulated by a non‐stationary Gamma process and random shocks are modeled as a homogeneous Poisson process. Preventive replacement (PR) and corrective replacement (CR) are accounted for in the developed maintenance strategy. The optimal periodic inspection interval, PR threshold, and the number of cumulative random shocks can be derived by minimizing the average long‐term maintenance cost rate. In the end, a numerical case is applied to demonstrate the availability of the developed models.

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Section: Introductionmentioning

confidence: 99%

“…Extensive studies have been undertaken on the reliability of dependent competing failure processes (DCFPs) 35–40 . For example, Che et al 41 .…”

Section: Introductionmentioning

confidence: 99%

Optimal condition‐based maintenance policy for systems with mutually dependent competing failure

Huang

2023

Quality & Reliability Eng

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“…Degradation modeling has been widely studied to evaluate the reliability in some electronics manufacturing or mechanical systems. The performance index of the degenerate system is usually assumed to be a quality characteristic that the degradation is resulted from a gradual accumulation of damage in a system's life cycle [6,31,33]. The failure occurs when the accumulative degradation reaches a specified threshold which is defined in terms of the requirement of running situation for the system.…”

Section: Introductionmentioning

confidence: 99%

Quasi-periodic Inspection and Preventive Maintenance Policy Optimisation for a system with Wiener Process degradation

Gao¹,

Wang²,

Zhang³

et al. 2023

Eksploatacja i Niezawodność – Maintenance and Reliability

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Periodic inspection policy is performed for some degradation systems to check their degradation states, whereas it is usually difficult to implement on time due to impact of some random factors. Inspections and some maintenance actions are implemented in an inspection window with random, and thus how to optimize the inspection windows and the degradation threshold of the system to perform preventive maintenance (PM) are beneficial in practice. To this end, an optimisation of quasiperiodic inspection and PM policy with inspection window is proposed for a degradation system whose degradation followed Wiener process with a linear drift. Assume that PM can change the degradation rate and inspections are randomly performed in each inspection window. After optimisation, the optimal interval of the inspection window, the degradation threshold of PM and PM policy are determined by minimising the long-term running cost rate of the system. Finally, modeling and optimisation are illustrated using the degradation process of an axial piston pump, and the sensitivity analysis of some key parameters is conducted.

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“…To accurately describe the reliability of the complex bladedisc system with different material properties, it is often necessary to collaboratively analyse multiple components [11][12][13]. However, the multi-component collaborative deterministic analysis is already very tedious, the computing efficiency and accuracy of conducting thousands of multi-component probabilistic analyses (reliability analysis) will become the technique bottleneck [14][15][16]. Moreover, although the computing efficiency can be improved if the reliability analysis of multiple components is performed independently, the result is definitely not a highly accurate reliability analysis due to the cut of correlation relationships; on the contrary, if the system reliability analysis is performed as a whole model, the existing computing devices can hardly bear the large computing tasks.…”

Section: Introductionmentioning

confidence: 99%

Fatigue reliability analysis of aeroengine blade-disc systems using physics-informed ensemble learning

Li,

Song,

Choy

et al. 2023

Phil. Trans. R. Soc. A.

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For the fatigue reliability analysis of aeroengine blade-disc systems, the traditional direct integral modelling methods or separate independent modelling methods will lead to low computational efficiency or accuracy. In this work, a physics-informed ensemble learning (PIEL) method is proposed, i.e. firstly, based on the physical characteristics of blade-disc systems, the complex multi-component reliability analysis is split into a series of single-component reliability analyses; moreover, the PIEL model is established by introducing the mapping of multiple constitutive responses and the multi-material physical characteristics into the ensemble learning; finally, the PIEL-based system reliability framework is established by quantifying the failure correlation with the Copula function. The reliability analysis of a typical aeroengine high-pressure turbine blade-disc system is regarded as an example to verify the effectiveness of the proposed method. Compared with the direct Monte Carlo, support vector regression, neural network, ensemble learning and physics-informed neural network, the proposed method exhibits the highest computing accuracy and efficiency, and is validated to be an efficient method for the reliability analysis of blade-disc systems. The current work can provide a novel insight for physics-informed modelling and fatigue reliability analyses. This article is part of the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 1)'.

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Reliability analysis of systems considering clusters of dependent degrading components

Cited by 39 publications

References 43 publications

Optimal condition‐based maintenance policy for systems with mutually dependent competing failure

Optimal condition‐based maintenance policy for systems with mutually dependent competing failure

Quasi-periodic Inspection and Preventive Maintenance Policy Optimisation for a system with Wiener Process degradation

Fatigue reliability analysis of aeroengine blade-disc systems using physics-informed ensemble learning

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