中国激光, 2020, 47 (11): 1104004, 网络出版: 2020-11-02   

利用FBG传感信号诊断滚动轴承故障的检测方法 下载: 688次

Detection Method Using FBG Sensing Signal to Diagnose Rolling Bearing Fault
作者单位
1 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
2 重庆邮电大学光纤通信技术重点实验室, 重庆 400065
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%.

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. 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.

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