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利用FBG传感信号诊断滚动轴承故障的检测方法

Detection Method Using FBG Sensing Signal to Diagnose Rolling Bearing Fault

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摘要

针对传统轴承故障诊断算法精度低、易受噪声干扰等问题,提出一种经验模态分解与卷积神经网络相结合的诊断方法。利用光纤布拉格光栅(FBG)获取轴承的振动信号,再由经验模态分解将信号分解为多个本征模态函数(IMF)分量,并提取有效信号,利用IMF分量的结构特性将IMF分量组合成矩阵,输入至改进的卷积神经网络中进行故障分类识别。实验结果表明,所提方法能有效识别正常轴承、故障轴承及复合故障轴承,其识别准确率大于91%。

Abstract

As a result of low accuracy and susceptibility to noise interference of traditional bearing fault diagnostic algorithms, a diagnosis method combining empirical mode decomposition and convolutional neural network is proposed. First, fiber Bragg grating (FBG) is used to obtain the vibration signal of the bearing, and then empirical mode decomposition is used to decompose the signal into multiple intrinsic mode function (IMF) components. After the extraction of useful signals, based on the structural characteristics of IMF components, the IMF components are combined into a matrix and input into the improved convolutional neural network for fault classification and recognition. The results show that the proposed method can effectively identify normal, faulty, and composite faulty bearings. Furthermore, the recognition accuracy of the proposed method is greater than 91%.

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中图分类号:TN253; TN911.7

DOI:10.3788/CJL202047.1104004

所属栏目:测量与计量

基金项目:国家自然科学基金、重庆市研究生科研创新项目;

收稿日期:2020-05-14

修改稿日期:2020-07-06

网络出版日期:2020-11-01

作者单位    点击查看

陈勇:重庆邮电大学工业物联网与网络化控制教育部重点实验室, 重庆 400065
安汪悦:重庆邮电大学工业物联网与网络化控制教育部重点实验室, 重庆 400065
刘焕淋:重庆邮电大学光纤通信技术重点实验室, 重庆 400065
陈亚武:重庆邮电大学工业物联网与网络化控制教育部重点实验室, 重庆 400065

联系人作者:陈勇(chenyong@cqupt.edu.cn)

备注:国家自然科学基金、重庆市研究生科研创新项目;

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引用该论文

Chen Yong,An Wangyue,Liu Huanlin,Chen Yawu. Detection Method Using FBG Sensing Signal to Diagnose Rolling Bearing Fault[J]. Chinese Journal of Lasers, 2020, 47(11): 1104004

陈勇,安汪悦,刘焕淋,陈亚武. 利用FBG传感信号诊断滚动轴承故障的检测方法[J]. 中国激光, 2020, 47(11): 1104004

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