Reliability modeling for multi-component system subject to dependent competing failure processes with phase-type distribution considering multiple shock sources

Lyu, Hao; Wang, Shuai; Yang, Zaiyou; Qu, Hongchen; Ma, Long

doi:10.1080/08982112.2022.2098043

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…turbine blades, turbine discs), the individual components such as blades and discs are often interrelated and jointly influence the system reliability [9,10]. 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].…”

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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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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“…Jiang et al 12 classified the shocks, and the degradation of the system occurred when the magnitude of the shock was greater than the first region. Lyu et al 13 combined the phase-type distribution survival function when studying the multi-component system subject to DCFP with multiple shock sources. The above research promotes the work on competing failure processes.…”

Section: Introductionmentioning

confidence: 99%

Reliability modeling for dependent competing failure processes considering random cycle times

Lyu,

Lu,

Xie

et al. 2023

Quality & Reliability Eng

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In this paper, a novel model is proposed for the reliability analysis of systems subjected to dependent competing failure processes at random cycle times. The model takes into account the effect of random cycle time on degradation rate and hard failure threshold. Random cycle time is the time required for the system to complete a run. With the continuous operation of the system in a period of cycle time, the degradation rate of the system is accelerated, and its resistance to random shocks is decreased, so the degradation rate and hard failure threshold are supposed to vary with the number of cycles. When the overall degradation exceeds the soft failure threshold, soft failure is triggered. When the shock magnitude is larger than the hard failure threshold, hard failure occurs. Combined with the stress‐strength interference model, the reliability function of the system is derived. Finally, a Micro‐Electro‐Mechanical System is used to verify the validity of the proposed model. The validity of the model is demonstrated by sensitivity analysis.

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Reliability modeling for failure correlations to a CNC lathe subsystem based on Pagerank Algorithm

Guo

Han

et al. 2023

Quality Engineering

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Reliability modeling for multi-component system subject to dependent competing failure processes with phase-type distribution considering multiple shock sources

Cited by 3 publications

References 27 publications

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

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

Reliability modeling for dependent competing failure processes considering random cycle times

Reliability modeling for failure correlations to a CNC lathe subsystem based on Pagerank Algorithm

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