BEARING FAULT DIAGNOSIS BASED ON AE-IFCM

Wang Jinhua; Wang Yuelong; Huang Tao; Cao Jie

doi:10.19912/j.0254-0096.tynxb.2020-1232

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Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (7) : 310-315. DOI: 10.19912/j.0254-0096.tynxb.2020-1232

BEARING FAULT DIAGNOSIS BASED ON AE-IFCM

Wang Jinhua¹, Wang Yuelong¹, Huang Tao², Cao Jie^1,3

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Abstract

The traditional fuzzy clustering method clustering the fault is based on the similarity between the original data. But in the fault diagnosis of rolling bearings, that way cannot extracted the deep features of the bearing data well. Especially, in the complex conditions such as coupling faults and weak faults, it is difficult to effectively distinguish the different faults features, which results in low accuracy. In order to solve that problem, we propose the AE-IFCM bearing fault diagnosis method. In this framework, the Auto-Encoder（AE） network is used to extract the deep features of the bearing fault samples, and then we utilize the improved FCM for fault diagnosis. It clusters the abstract features extracted by the AE network to maximize the utilization of the sample data and reduce the risk of the model falling into a local minimum. Experiments in the Case Western Reserve university bearing fault data collection show that AE-IFCM can improve the accuracy of bearing fault diagnosis.

Key words

fault diagnosis / autoencoder / fuzzy clustering / roll bearing

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Wang Jinhua, Wang Yuelong, Huang Tao, Cao Jie. BEARING FAULT DIAGNOSIS BASED ON AE-IFCM[J]. Acta Energiae Solaris Sinica. 2022, 43(7): 310-315 https://doi.org/10.19912/j.0254-0096.tynxb.2020-1232

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