A novel complex network community clustering method for fault diagnosis. (1st January 2023)

• Record Type: Journal Article
• Authors: Chen, Hongming; Lei, Zihao; Tian, Feiyu; Wen, Guangrui; Feng, Ke; Zhang, Yongchao; Liu, Zheng; Chen, Xuefeng
• Is Part Of: Measurement science & technology. Volume 34:Number 1(2023)
• Publication Date: 2023-01-01
• Subjects: fault diagnosis -- community clustering -- modified Fast Newman algorithm -- reliability judgment; Physical measurements -- Periodicals; Scientific apparatus and instruments -- Periodicals; Equipment and Supplies -- Periodicals; Science -- instrumentation -- Periodicals; Technology -- instrumentation -- Periodicals; Mesures physiques -- Périodiques; Physical measurements; Scientific apparatus and instruments; Periodicals; 502.87
• DOI: 10.1088/1361-6501/ac97b2
• Languages: English
• ISSNs: 0957-0233

Abstract

Abstract: The complex network, as a method for the analysis of nonlinear and non-stationary signals, overcomes the shortcomings of traditional time-frequency analysis methods and proves its effectiveness in mechanical fault diagnosis. Community clustering, a type of complex network, has made great progress in recent years. However, the existing community clustering algorithms have disadvantages in that they lack significant global extreme value and huge search spaces. Therefore, a Fast Newman algorithm based on reliability judgment is proposed. Starting from the community structure characteristics of the complex network, with the fault sample as a network node, the relationship between the samples as a connected edge and a complex network model of fault data is established. Clusters in troubleshooting are transformed into community structure discovery in the network. Firstly, the initial division of the community is obtained by measuring the distance between the samples. Then, the modularity index of the network is used as a standard function of the community division, and the bottom-up community merger is performed. The local edge density index is used for reliability determination before each combination to achieve global optimization, and the network block structure is the most obvious. Finally, with all the data merged into one community, the optimal division of the community structure is obtained, while accurate community clustering and fault diagnosis is realized. The benchmark graphs for testing community detection (Lancichinetti–Fortunato–Radicchi benchmark standard test network, LFR) and different fault data of rolling bearings under multiple operating conditions are applied to verify the effectiveness of this method; the results prove that the modified Fast Newman algorithm has better clustering effects and a higher accuracy rate than the original algorithm. Compared with K -means clustering and fuzzy clustering, the modified Fast Newman algorithm achieves higher performance in fault diagnosis of rolling bearings under multiple operating conditions.