基于改进变分模态分解和SVM的断路器机械故障振动分析

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摘要针对传统信号分解导致断路器机械故障振动分析特征提取不准确，故障诊断精度低的缺点，提出将改进变分模态分解能量熵与支持向量机相结合的断路器故障诊断新方法。利用量子粒子群优化变分模态分解(VMD)参数设置问题，获取最佳模态个数及惩罚因子。为验证该算法在模态混叠及噪声干扰方面的优势，将其应用在10 kV高压断路器ZN63A-12上，对断路器采集到的正常、传动机构卡涩及基座螺丝松动三种振动信号进行分析。用改进VMD能量熵提取断路器振动特征，并输入支持向量机确定故障状态。实验结果表明，所提方法在少量样本情况下仍能有效提取断路器的运行状态并对故障进行分类，具有较高的应用价值。

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	田书
	康智慧

关键词 ：变分模态分解, 断路器, 振动信号, 支持向量机

Abstract：Aiming at shortcomings of traditional signal decomposition causing circuit breaker’s incorrect mechanical fault vibration analysis feature extraction and lower fault diagnosis accuracy, a new method for circuit breaker fault diagnosis based on the improved variational mode decomposition (VMD) energy entropy combined with support vector machine (SVM) was proposed.The setting of VMD parameters was optimized by using the quantum-behaved particle swarm optimization (QPSO) method to obtain the optimal mode number and penalty factor.In order to verify this method’s advantages in suppressing mode aliasing and noise interference, it was applied to analyze a 10 kV high voltage circuit breaker 10kV high voltage circuit breaker ZN63A-12’s three kinds of vibration signals collected in cases of normal, transmission mechanism jamming and base screw loosing.The improved VMD energy entropy was used to extract vibration features of the circuit breaker ZN63A-12, and these features were input into a support vector machine to determine ZN63A-12’s fault state.Test results showed that the proposed method can be used to effectively extract fault vibration features of the circuit breaker, and classify faults under the condition of a few samples, so it has higher application value.
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Key words： variational mode decomposition (VMD) circuit breaker vibration signal support vector machine (SVM)

收稿日期: 2018-04-11 出版日期: 2019-11-28

引用本文:

田书，康智慧. 基于改进变分模态分解和SVM的断路器机械故障振动分析[J]. 振动与冲击, 2019, 38(23): 90-95.
TIAN Shu, KANG Zhihui. Circuit breaker mechanical fault vibration analysis based on improved variational mode decomposition and SVM. JOURNAL OF VIBRATION AND SHOCK, 2019, 38(23): 90-95.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2019/V38/I23/90

[1] 付荣荣, 赵莉华, 荣强,等. 高压断路器操作机构机械特性研究[J]. 高压电器, 2017, 53(5): 56-62.
FU Rongrong, ZHAO Lihua, RONG Qiang, et al. Study on mechanical characteristics of operating mechanism of high voltage circuit breaker [J]. High Volltage Apparatus, 2017, 53(5): 56-62.
[2] RUNDE M, OTTESEN G E, SKYBERG B, et al. Vibration analysis for diagnostic testing of circuit-breakers [J]. IEEE Transactions on Power Delivery, 1996, 11(4): 1816-1823.
[3] 王建华, 张国钢, 耿英三, 等. 智能电器最新技术研究及应用发展前景[J]. 电工技术学报, 2015, 30(9): 1-11.
WANG Jianhua, ZHANG Guogang, GENG Yingsan, et al. The latest technology research and application prospects of the intelligent electrical apparatus [J]. Transactions of China Electrotechnical Society, 2015, 30(9): 1-11.
[4] 张弛. 高压断路器在线监测与故障诊断系统研究[D]. 北京:北京交通大学, 2007.
ZHANG Chi. The research of On-line monitoring and fanlts diagnostics for high voltage circuit breaker [D]. Beijing: Beijing Jiaotong University, 2007.
[5] 王飞, 高文胜, 赵宇明, 等. 基于振动信号极值点发生时刻的真空断路器状态评估[J]. 高压电器, 2017, 53(3): 248-253.
WANG Fei, GAO Wensheng, ZHAO Yuming, et al. State evaluation of vacuum circuit breaker based on the occurrence time of extreme point of vibration signal [J]. High Voltage Apparatus, 2017, 53(3): 248-253.
[6] 陈远洋, 鲍晓华, 高格,等. 经验模态分解在EAST超导线圈电压信号分析中的应用[J]. 电气工程学报, 2018, 13(02): 9-14.
CHEN Yuanyang, BAO Xiaohua, GAO Ge, et al. Application of EMD algorithm to analyze voltage signals of EAST superconducting coils [J]. Journal of Electrical Engineering, 2014, 29(8): 77-81.
[7] WU Z H, HUANG N E. Ensemble empirical mode decomposition: a noise assisted data analysis method [J]. Advances in Adaptive Data Analysis, 2009, 1(1): 1-41.
[8] DRAGOMIRETSKIY K, ZOSSO D. Variational mode decomposition [J]. IEEE Transactions on Signal Processing, 2014, 62(3): 531-544.
[9] 钱林, 康敏, 傅秀清, 等. 基于VMD的自适应形态学在轴承故障诊断中的应用[J]. 振动与冲击, 2017, 36(3): 227-233.
QIAN Lin, KANG Min, FU Xiuqing, et al. Application of adaptive morphology in bearing fault diagnosis based on VMD [J]. Journal of Vibration and Shock, 2017, 36(3): 227-233.
[10] 潘凌云, 赵岩, 高丙坤. 一种基于VMD和小波去噪的管道泄漏检测算法[J]. 自动化技术与应用, 2017, 36(9): 1-5.
PAN Lingyun, ZHAO Yan, GAO Bingkun. A pipeline leak detection algorithm based on VMD and wavelet denoising [J]. Techniques of Automation and Applications, 2017, 36(9): 1-5.
[11] 范启蒙, 尹成友, 廖飞龙. 基于量子粒子群优化的短波相控阵天线的激励优化研究[J]. 电子与信息学报, 2017, 39(7): 1769-1773.
FAN Qimeng, YI Chengyou, LIAO Feilong. Analysis of excitation optimization of short wave phased array based on quantum-behaved particle swarm optimization [J]. Journal of Electronics & Information Technology, 2017, 39(7): 1769-1773.
[12] 张小蓟, 张歆, 孙进才. 基于IMF能量熵的目标特征提取与分类方法[J]. 计算机工程与应用, 2008, 44(4): 68-69.
ZHANG Xiao Ji, SUN Jincai. Feature extraction and classification experiment based on energy entropy of IMF’s [J]. Computer Engineering and Applications, 2008, 44( 4): 68- 69.
[13] 李阳海, 王广庭, 卢双龙, 等. 变压器绕组松动的振动实验分析[J]. 噪声与振动控制, 2016, 36(5): 34-37.
LI Yanghai, WANG Guangting, LU Shuanglong, et al. Experimental Research of Transformer’s Vibration due to the Winding Looseness [J]. Noise and Vibration Control, 2016, 36(5): 34-37.
[14] 徐乐, 邢邦圣, 郎超男, 等. LMD能量熵和SVM相结合的滚动轴承故障诊断[J]. 机械科学与技术, 2017, 36(6): 915-918.
XU Le, XING Bangsheng, LANG Chaonan, et al. Fault Diagnosis of Rolling Bearing Combined LMD Energy Entropy and SVM [J]. Mechanical Science and Technology for Aerospace Engineering, 2017, 36(6): 915-918.
[15] 陈伟根, 邓帮飞, 杨彬. 基于振动信号经验模态分解及能量熵的高压断路器故障识别[J]. 高压电器, 2009, 45(2): 90-93.
CHEN Weigen, DENG Bangfei, YANG Bin. Fault recognition for high voltage circuit breaker based on EMD of vibration signal and energy entropy characteristic [J]. High Volltage Apparatus, 2009, 45(2): 90-93.
[16] 黄建. 特征评估高压断路器机械故障诊断方法的研究[J]. 高压电器, 2015, 51(12): 89-95.
HUANG Jian. Research on machinery fault diagnosis of high voltage circuit breaker based on feature evaluation [J]. High Volltage Apparatus, 2015, 51(12): 89-95.
[17] PSORAKIS I, DAMOULAS T, GIROLAMI M A. Multiclass relevance vector machines: sparsity and accuracy [J]. IEEE Transactions on Neural Networks, 2010, 21(10): 1588-1598.
[18] ZHOU T Y, TAO D C, WU X D. Compressed labeling on distilled labelsets for multi-label learning [J]. Machine Learning, 2012, 88(1-2): 1-58.
[19] 周小龙, 刘薇娜, 姜振海, 等. 基于改进HHT和马氏距离的齿轮故障诊断[J]. 振动与冲击, 2017, 36(22): 218-224.
ZHOU Xiaolong, LIU Weina, JIANG Zhenhai, et al. Gear fault diagnosis based on improved HHT and mahalanobis distance [J]. Journal of Vibration and Shock, 2017, 36(22): 218-224.
[20] SEBALD D J, BUKLEW J A. Support vector machine techniques for nonlinear equalization [J]. IEEE Transactions on,Signal Processing 2000, 48(11): 3217-3226.
[21] 刘畅, 伍星, 刘韬, 等. 基于近似等距投影和支持向量机的滚动轴承故障诊断[J]. 振动与冲击, 2018, 37(5) : 234-239.
LIU Chang, WU Xing, LIU Tao, et al. Fault diagnosis of rolling bearings based on near-isometric projection and support vector machine [J]. Journal of Vibration and Shock, 2018, 37(5): 234-239.