A Model for Flywheel Fault Diagnosis Based on Fuzzy Fault Tree Analysis and Belief Rule Base

Cheng, Xiaoyu; Liu, Shanshan; He, Wei; Peng, Zhang; Xu, Bing; Xie, Yawen; Song, Jiayuan

doi:10.3390/machines10020073

Cited by 12 publications

(7 citation statements)

References 25 publications

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“…However, when the model has many parameters and the expert has insufficient experience and knowledge, the initial setting of the parameters is not reasonable, which may affect the accuracy of the model diagnosis. Therefore, this paper proposes an optimization process of the model to improve the diagnostic accuracy of the model using a projection covariance matrix adaptation evolution strategy (P-CMA-ES) to optimize the parameters of the model [28,29]. The model's parameters that need to be optimized need to satisfy the following conditions.…”

Section: The Model Optimization Processmentioning

confidence: 99%

A WSN Node Fault Diagnosis Model Based on BRB with Self-Adaptive Quality Factor

Sun¹,

Xiang²,

He³

et al. 2023

Computers, Materials &Amp; Continua

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Wireless sensor networks (WSNs) operate in complex and harsh environments; thus, node faults are inevitable. Therefore, fault diagnosis of the WSNs node is essential. Affected by the harsh working environment of WSNs and wireless data transmission, the data collected by WSNs contain noisy data, leading to unreliable data among the data features extracted during fault diagnosis. To reduce the influence of unreliable data features on fault diagnosis accuracy, this paper proposes a belief rule base (BRB) with a selfadaptive quality factor (BRB-SAQF) fault diagnosis model. First, the data features required for WSN node fault diagnosis are extracted. Second, the quality factors of input attributes are introduced and calculated. Third, the model inference process with an attribute quality factor is designed. Fourth, the projection covariance matrix adaptation evolution strategy (P-CMA-ES) algorithm is used to optimize the model's initial parameters. Finally, the effectiveness of the proposed model is verified by comparing the commonly used fault diagnosis methods for WSN nodes with the BRB method considering static attribute reliability (BRB-Sr). The experimental results show that BRB-SAQF can reduce the influence of unreliable data features. The self-adaptive quality factor calculation method is more reasonable and accurate than the static attribute reliability method.

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Section: The Model Optimization Processmentioning

confidence: 99%

A WSN Node Fault Diagnosis Model Based on BRB with Self-Adaptive Quality Factor

Sun¹,

Xiang²,

He³

et al. 2023

Computers, Materials &Amp; Continua

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“…First, the problem of integrating the fault tree mechanism into the hierarchical BRB expert knowledge base must be addressed. In hierarchical BRB, the relationship between input and output is expressed by some belief rules, which experts usually decide based on empirical knowledge (with data from the system model) [30]. However, when applying the hierarchical BRB model in milling fault detection methods, it is difficult to embed expert knowledge into the model of the milling fault detection method.…”

Section: ) How To Integrate the Fault Tree Mechanism Into The Hierarc...mentioning

confidence: 99%

Milling Fault Detection Method Based on Fault Tree Analysis and Hierarchical Belief Rule Base

Cheng¹,

Long²,

Wang³

et al. 2023

Computer Systems Science and Engineering

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Expert knowledge is the key to modeling milling fault detection systems based on the belief rule base. The construction of an initial expert knowledge base seriously affects the accuracy and interpretability of the milling fault detection model. However, due to the complexity of the milling system structure and the uncertainty of the milling failure index, it is often impossible to construct model expert knowledge effectively. Therefore, a milling system fault detection method based on fault tree analysis and hierarchical BRB (FTBRB) is proposed. Firstly, the proposed method uses a fault tree and hierarchical BRB modeling. Through fault tree analysis (FTA), the logical correspondence between FTA and BRB is sorted out. This can effectively embed the FTA mechanism into the BRB expert knowledge base. The hierarchical BRB model is used to solve the problem of excessive indexes and avoid combinatorial explosion. Secondly, evidence reasoning (ER) is used to ensure the transparency of the model reasoning process. Thirdly, the projection covariance matrix adaptation evolutionary strategies (P-CMA-ES) is used to optimize the model. Finally, this paper verifies the validity model and the method's feasibility techniques for milling data sets.

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“…A fault diagnosis method using Bayesian networks as a model bridge was proposed through the mapping relationship between fuzzy fault trees and BRBs by Cheng, X., Liu, S. et al. [ 21 ]. However, these methods have high modelling requirements.…”

Section: Related Workmentioning

confidence: 99%