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基于小波时频图和卷积神经网络的断路器故障诊断分析

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摘要高压断路器操动机构振动信号为非平稳性信号，蕴含着丰富的操动机构工作状态的信息，对操动机构工作状态的检验辨识具有重大意义。提出一种基于小波时频图和卷积神经网络的断路器故障诊断方法。对操动机构振动信号进行连续小波变换生成时频图（CWT），并对时频图进行统一压缩预处理；将预处理后的时频图作为特征图输入卷积神经网络AlexNet模型；通过对网络参数的调整，逐步改进网络模型，有监督地实现对操动机构故障状态的辨识诊断。结果表明，该方法能够有效地运用于断路器操动机构故障辨识诊断，与小波频带能量-RBF、小波频带能量-SVM的故障识别相比，故障识别准确率最高。

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	鄢仁武
	林穿
	高硕勋
	罗家满
	李天建
	夏正邦

关键词 ：振动信号, 小波变换, 卷积神经网络, 故障诊断

Abstract：The vibration signal of the operating mechanism of high voltage circuit breaker is non-stationary, which contains abundant information of the operating state of the operating mechanism.It is of great significance for the inspection and identification of the operating state of the operating mechanism.This paper presented a new method of applying wavelet time-frequency diagram and convolution neural network to fault diagnosis of circuit breakers.Continuous wavelet transform(CWT) was used to analyze vibration signals of operating mechanism and get time-frequency representation.And the time-frequency representations were compressed to the appropriate size.After that, all the compressed time-frequency representations were taken as input feature maps of convolution neural network AlexNet model.By adjusting the network parameters, the network model was improved step by step, and the fault state identification and diagnosis of the operating mechanism was realized with supervision.The results show that this method can be effectively applied to fault identification and diagnosis of circuit breaker operating mechanism.Compared with wavelet band energy-RBF, wavelet band energy-SVM algorithm, the accuracy of the method in the paper is the highest.

Key words： vibration signal wavelet transform convolutional neural network fault diagnosis

收稿日期: 2018-12-12 出版日期: 2020-05-15

引用本文:

鄢仁武，林穿，高硕勋，罗家满，李天建，夏正邦. #br# 基于小波时频图和卷积神经网络的断路器故障诊断分析[J]. 振动与冲击, 2020, 39(10): 198-205.
YAN Renwu,LIN Chuan,GAO Shuoxun,LUO Jiaman,LI Tianjian,XIA Zhengbang. Fault diagnosis and analysis of circuit breaker based on wavelet time-frequency representations and convolution neural network. JOURNAL OF VIBRATION AND SHOCK, 2020, 39(10): 198-205.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2020/V39/I10/198

[1]张永奎，赵智忠，冯旭，等.基于分合闸线圈电流信号的高压断路器机械故障诊断 [J].高压电器，2013.49（2）：37-41.

Zhang Yongkui, Zhao Zhizhong, Feng Xu, et al.Mechanical Fault Diagnosis of High Voltage Circuit Breakers Based on Opening/Closing Coil Current Parameters [J].High Voltage Apparatus,2013.49（2）：37-41.

[2]李鹏飞，周文俊，曾国，等.高压断路器合闸弹簧动态特性及储能状态检测方法 [J].电工技术学报，2016,31（3）：104-112.

Li Pengfei, Zhou Wenjun, Zeng Guo, et al.The Dynamic Characteristics and Energy Storage State Detection Method of High-voltage Circuit Breaker Closing Spring [J].Transactions of China Electrotechnical Society, 2016,31（3）：104-112.

[3]HUANG N T,CHEN H J,ZHANG S X,et al.Mechanical Fault Diagnosis of High Voltage Circuit Breakers Based on Wavelet Time-Frequency Entropy and Oneclass Support Vector Machine [J].Entropy,2015,18(1):1-17.

[4]孙一航，武建文，廉世军，张路明.结合经验模态分解能量总量法的断路器振动信号特征向量提取 [J].电工技术学报，2014,29（3）：228-236.

Sun Yihang, Wu Jianwen, Lian Shijun, Zhang Luming.Extraction of Vibration Signal Feature Vector of Circuit Breaker Based on Empirical Mode Decomposition Amount of Energy [J].Transactions of China Electrotechnical Society,2014,29（3）：228-236.

[5]张佩，赵书涛，申路，赵现平.基于改进EEMD的高压断路器振声联合故障诊断方法 [J].电力系统保护与控制,2014,42(8):077-081.

Zhang Pei, Zhao Shutao, Shen Lu, Zhao Xianping. Research on vibration and acoustic joint mechanical fault diagnosis method of high voltage circuit breaker based on improved EEMD [J]. Power System Protection and Control,2014,42(8):077-081.

[6]常亮，邓小名，周明全，等.图像理解中的卷积神经网络 [J].自动化学报,2016,42(9):1300-1312.

Chang Liang, Deng Xiaoming, Zhou Mingquan, et al. Convolutional Neural Networks in Image Understanding [J]. ACTA AUTOMATICA SINICA, 2016,42(9):1300-1312.

[7]ABDEL-HAMID O,MOHAMED A R,JIANG H.Convolutional neural networks for speech recognition [J].IEEE ACM Transactions on Audio,Speech,and Language Processing,2014,22(10):1538-1545.

[8]LIU M Y,LI S X,SHAN S G,et al.AU-inspired deep networks for facial expression feature learning[J].Neurocomputing,2015,159(8):126-136.

[9]王丽华，谢阳阳，周子贤，张永宏，赵晓平.基于卷积神经网络的异步电机故障诊断 [J].振动、测试与诊断,2017,37(6):1208-1215.

Wang Lihua, Xie Yangyang, Zhou Zixian, Zhang Yonghong, Zhao Xiaoping. Motor Fault Diagnosis Based on Convolutional Neural Networks[J]. Journal of Vibration, Measurement & Diagnosis,2017,37(6):1208-1215.

[10]郑炳集，熊邦书，欧巧凤，陈新云.基于时频图和CNN的滚动轴承故障诊断 [J].南昌航空大学学报：自然科学版,2018,32(2):86-91.

Liu Bingji, Xiong Bangshu, Ou Qiaofeng, Chen Xinyun. Fault Diagnosis of Ｒolling Bearing Based on Time-frequency Ｒepresentations and CNN [J].Journal of Nanchang Hangkong University:Natural Sciences,2018,32(2):86-91.

[11]李泽龙，杨春节，刘文辉，周恒，李宇轩.基于 LSTM-RNN 模型的铁水硅含量预测[J].化工学报，2018,69（3）：992-997.

LI Zelong, YANG Chunjie, LIU Wenhui, ZHOU Heng, LI Yuxuan.Research on hot metal Si-content prediction based on LSTM-RNN[J].CIESC Journal,2018,69（3）：992-997.

[12]Hinton G E，Li Deng，Dahl G E． Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups［J］．Signal Processing，Magazine，2012，29(6)，82-97．

[13]左东奇，韩霖，陈科，李程，花瞻，林江莉.基于卷积神经网络提取超声图像甲状腺结节钙化点的研究[J].生物医学工程学杂志，2018,10(35):679-687.

Zuo Dongqi, Han Lin, Chen Ke, Li Cheng, Hua Zhan, Lin Jiangli. Extraction of calcification in ultrasonic images based on convolution neural network[J]. Journal of Biomedical Engineering, 2018,10(35):679-687.

[14]罗明柱，肖业伟.全卷积神经网络的多尺度人脸检测的研究[J].计算机工程与应用,2018,11:1-7

LUO Mingzhu, XIAO Yewei. Multi-scale Face Detection of Full Convolution Neural Network. Computer En-

gineering and Applications,2018,11:1-7.

[15]JAWADE A,PARASKAR S,JADHAV S.Artificial neural network based induction motor fault classifier using continuous wavelet transform ［J］．Systems Science ＆ Control Engineering，2014，2(1) : 684 －690．

[16]张浩,赵莉华,景伟,阳薇,荣强,付荣荣.高压断路器弹簧机构电磁铁部分动态数学模型研究 [J].高压电器，2016，52（7）：0110-0115.

Zhang Hao, Zhao Lihua,Jing Wei, Yang Wei, Rong Qiang, Fu Rongrong. Dynamic Model of Electromagnetic Component in Spring Operating Mechanism of High-voltage Circuit Breaker [J].High Voltage Apparatus,2016，52（7）：0110-0115.

[17]袁建虎，韩涛，唐建，安立周.基于小波时频和CNN的滚动轴承智能故障诊断方法 [J].机械设计与研究,2017,33(2):93-97.

Yuan Jianhu, Han Tao, Tang Jian, An Lizhou.An Approach to Intelligent Fault Diagnosis of Ｒolling Bearing Using Wavelet Time-Frequency Ｒepresentations and CNN [J]. Machine Design and Research, 2017,33(2):93-97.

[18]卢伟,胡海阳,王家鹏,王玲,Yiming Deng.基于卷积神经网络面部图像识别的拖拉机驾驶员疲劳检测 [J].农业工程学报,2018,34(7):192-199.

Lu Wei, Hu Haiyang, Wang Jiapeng, Wang Ling, Yiming Deng. Tractor driver fatigue detection based on convolution neural network and facial image recognition [J]. Transactions of the Chinese Society of Agricultural Engineering, 2018,34(7):192-199.

[19]CHAN T H,JIA K,GAO S H.PCANet:A simple deep learning baseline for image classification [J].IEEE Transactions Image Processing,2014,24(12):50017-5032.

[20]Krizhevsky A, Sutskever I, Hinton G E. ImageNet classiflcation with deep convolutional neural networks. In: Proceedings of Advances in Neural Information Processing Systems 25. Lake Tahoe, Nevada, USA: Curran Associates, Inc.,2012. 1097-1105

[21]王功鹏，段荫，牛常勇.基于卷积神经网络的随机梯度下降算法 [J].计算机工程与设计，2018.39（2）:441-445.

Wang Gongpeng, Duan Meng, Niu Changyong. Stochastic gradient descent algorithm based on convolution neural networ [J]. COMPUTER ENGINEERING AND DESIGN, 2018.39（2）:441-445.

[22]党宇，张继贤，邓喀中，赵有松，余凡.基于深度学习AlexNet的遥感影像地表覆盖分类评价研究 [J].地球信息科学.2017，19（11）：1530-1537.

Dang Yu, Zhang Jixian, Deng Kazhong, Zhao Yousong, Yu FAN. Study on the Evaluation of Land Cover Classification using Remote Sensing Images Based on AlexNet [J]. Journal of Geo-information Science,2017，19（11）：1530-1537.

[23]周敬一,郭燕,丁友东.基于深度学习的中文影评情感分析[J].上海大学学报（自然科学版）,2018,24(5):703-712.

Zhou Jingyi, Guo Yan, Ding Youdong. Sentiment analysis of Chinese movie reviews based on deep learning [J]. Journal of Shanghai University: Natural Science Edition, 2018,24(5):703-712.