Reliability Analysis of Complex Systems with Failure Propagation

Che, Changchang; Wang, Huawei; Ni, Xiaomei

doi:10.14429/dsj.69.12145

Cited by 4 publications

(2 citation statements)

References 3 publications

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“…The performance of their model is also compared with SVMs, and it is concluded that their proposed method is promising in the field of prognostics. In another study by Che et al [119], the SRA of complex systems with failure propagation is investigated using DBNs. The DBN in their research is applied to extract features between health monitoring data and the PF.…”

Section: Dbnmentioning

confidence: 99%

Deep learning-based methods in structural reliability analysis: a review

Afshari

Zhao

Zhuang

et al. 2023

Meas. Sci. Technol.

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One of the most significant and growing research fields in mechanical and civil engineering is Structural Reliability Analysis (SRA). A reliable and precise SRA usually has to deal with complicated , aand numerically expensive problems. Artificial intelligence-based (AI) nd specifically, Deep learning-based (DL) methods, have been applied to the SRA problems to reduce the computational cost and to improve the accuracy of reliability estimation as well. This article reviews the recent advances in using DL models in SRA problems. The review includes the most common categories of DL-based methods used in SRA. More specifically, the application of supervised methods, unsupervised methods, and hybrid deep learning methods in SRA are explained. In this paper, the supervised methods for SRA are categorized as Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN ), Long short-term memory (LSTM), Bidirectional (Bi-LSTM) and Gated recurrent units (GRU). For the unsupervised methods, we have investigated methods such as Generative Adversarial Network (GAN), Autoencoders (AE), Self-Organizing Map (SOM), Restricted Boltzmann Machine (RBM), and Deep Belief Network (DBN). We have made a comprehensive survey of these methods in SRA. Aiming towards an efficient SRA, deep learning-based methods applied for approximating the limit state function (LSF) with First/Second Order Reliability Methods (FORM/SORM), Monte Carlo simulation (MCS), or MCS with importance sampling (IS). Accordingly, the current paper focuses on the structure of different DL-based models and the applications of each DL method in various SRA problems. This survey helps researchers in mechanical and civil engineering, especially those who are engaged with structural and reliability analysis or dealing with quality assurance problems.

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Section: Dbnmentioning

confidence: 99%

Deep learning-based methods in structural reliability analysis: a review

Afshari

Zhao

Zhuang

et al. 2023

Meas. Sci. Technol.

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“…The high degree of structural and functional coupling between the component units provide the possibility of failure propagation. 1 During operation, any small failure or abnormal state may be propagated, diffused, accumulated and amplified in the system, thereby potentially causing a series of chain reactions. [2][3][4] If not dealt with in time, such chain reactions will cause a system to partially or even entirely collapse, and the resulting downtime loss and maintenance costs are difficult to estimate.…”

Section: Introductionmentioning

confidence: 99%

Application of hypergraph theory in the analysis of the failure propagation and diffusion behaviour of machining centre

Zhang

Zhou

2021

Quality & Reliability Eng

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To improve the rationality of failure diagnosis, this research uses hypergraph theory as basis in proposing a method of analysing failure propagation behaviour to study the failure propagation mechanism from the system perspective. A hierarchical structure model is constructed by failure correlation analysis, matrix transformation and decomposition. The probability of failure is determined by considering the influence of multiple truncation data. On this basis, hypergraph theory is used to calculate the one-step and cumulative coefficient of failure propagation and diffusion. The influence degree of failure is calculated by integrating the edge betweenness. Using the influence degree of failure as an indicator, failure propagation and diffusion behaviour are analysed, and critical failure nodes and paths are identified. Lastly, a machining centre is used as an example for specific application. The results are compared with the ranking results of critical failure propagation paths determined by other methods, and the effectiveness of the proposed method is verified.

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