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ISSN 0254-0096 CN 11-2082/K

太阳能学报 ›› 2022, Vol. 43 ›› Issue (7): 310-315.DOI: 10.19912/j.0254-0096.tynxb.2020-1232

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基于AE-IFCM的轴承故障诊断方法

王进花1, 王跃龙1, 黄涛2, 曹洁1,3   

  1. 1.兰州理工大学电气工程与信息工程学院,兰州 730050;
    2.中国市政工程西北设计研究院有限公司,兰州 730000;
    3.甘肃省制造信息工程研究中心,兰州 730050
  • 收稿日期:2020-11-16 出版日期:2022-07-28 发布日期:2023-01-28
  • 通讯作者: 王进花(1976—),女,博士、副教授,主要从事风力发电机故障诊断方面的研究。wjh0615@lut.edu.cn
  • 基金资助:
    国家自然科学基金(62063020; 61763028); 甘肃省自然科学基金(20JR5RA463)

BEARING FAULT DIAGNOSIS BASED ON AE-IFCM

Wang Jinhua1, Wang Yuelong1, Huang Tao2, Cao Jie1,3   

  1. 1. College of Electrical & Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China;
    2. China Municipal Engineering Northwest design and Research Institute Co., Ltd., Lanzhou 730000, China;
    3. Engineering Research Center of Manufacturing Information of Gansu Province, Lanzhou 730050, China
  • Received:2020-11-16 Online:2022-07-28 Published:2023-01-28

摘要: 针对传统模糊聚类(FCM)方法对故障进行聚类的依据是原始数据之间的相似性,在滚动轴承的故障诊断中无法提取轴承数据的深层特征,对于耦合故障、微弱故障等复杂情况下,不同故障的特征难以有效区分,导致故障诊断准确率较低的问题,提出AE-IFCM轴承故障诊断方法。利用自动编码器(AE)网络提取轴承故障的样本特征,再利用改进的FCM(IFCM)进行故障诊断,通过对AE网络提取的抽象特征聚类,不仅可最大限度地利用样本数据,也能降低模型陷入局部极小值的风险。通过在凯斯西储大学轴承故障数据集中的实验表明,AE-IFCM能提高轴承故障诊断的准确性。

关键词: 故障诊断, 自编码器, 模糊聚类, 滚动轴承

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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