基于振动信号时频分解-样本熵的受电弓裂纹故障诊断
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摘要
构建了基于时频分解-样本熵测度的受电弓振动信号故障特征提取模型。对振动信号进行聚合经验模态分解,接着对分解得到的本征模态函数计算参数优化后的样本熵特征。将获取的故障特征输入基于粒子群参数优化的支持向量机(PSO-SVM)进行受电弓故障识别分析。结果发现,基于受电弓顶管振动信号的EEMD样本熵故障诊断效果较好,而碳滑板振动信号诊断效果较差。针对这一特点,利用二代小波样本熵进行优化,进一步提高了碳滑板振动信号故障诊断结果,验证了现代时频分析算法与信息熵联合的诊断方法在受电弓振动信号特征提取与故障诊断的可行性与有效性。
A fault feature extraction model of pantograph vibration signal based on time-frequency decomposition and sample entropy was constructed. Firstly, ensemble empirical mode decomposition was conducted for vibration signal, then sample entropy was calculated after optimizing parameters for the intrinsic modal function obtained by EEMD. The sample entropy features were input to the support vector machine(PSO-SVM) based on particle swarm optimization(PSO) to identify the pantograph fault identification. The results show that the EEMD sample entropy fault diagnosis based on the pantograph panhead top pipe vibration signal has good accuracy, and the carbon contact strip vibration signal has poor diagnosis effect. According to this, the second-generation wavelet sample entropy was used to optimize and further improve the fault diagnosis results of carbon contact strip vibration signal. It verified the feasibility and effectiveness of the combination of the modern time-frequency analysis algorithm and the information entropy in the feature extraction and fault diagnosis of pantograph fault vibration signals.
A fault feature extraction model of pantograph vibration signal based on time-frequency decomposition and sample entropy was constructed. Firstly, ensemble empirical mode decomposition was conducted for vibration signal, then sample entropy was calculated after optimizing parameters for the intrinsic modal function obtained by EEMD. The sample entropy features were input to the support vector machine(PSO-SVM) based on particle swarm optimization(PSO) to identify the pantograph fault identification. The results show that the EEMD sample entropy fault diagnosis based on the pantograph panhead top pipe vibration signal has good accuracy, and the carbon contact strip vibration signal has poor diagnosis effect. According to this, the second-generation wavelet sample entropy was used to optimize and further improve the fault diagnosis results of carbon contact strip vibration signal. It verified the feasibility and effectiveness of the combination of the modern time-frequency analysis algorithm and the information entropy in the feature extraction and fault diagnosis of pantograph fault vibration signals.
引文
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[3]AYDIN I,KARAKOSE M,AKIN E.A new contactless fault diagnosis approach for pantograph-catenary system using pattern recognition and image processing methods[J].Advances in Electrical and Computer Engineering,2014,14(3):79-88.
[4]小山達弥,臼田隆之,池田充.地上モニタリングによるパンタグラフの異常検出[J].Rolling Stock&Machinery,2012,20(11):17-21.
[5]SAMI B,ALBERTO L,MAURO P,et al.Wavelet multiresolution analysis for monitoring the occurrence of arcing on overhead electrified railways[J].Proceedings of the Institution of Mechanical Engineers Part F Journal of Rail&Rapid Transit,2003,217(3):231-241.
[6]AYDIN I,CELEBI S B,BARMADA S,et al.Fuzzy integralbased multi-sensor fusion for arc detection in the pantographcatenary system[J].Proceedings of the Institution of Mechanical Engineers Part F Journal of Rail&Rapid Transit,2016,232(1):159-170.
[7]SANTAMATO G,GABARDI M,SOLAZZI M,et al.Approaches to the detectability of faults in railway pantograph mechanism[M].Berlin:Springer International Publishing,2017.
[8]袁宪锋,宋沐民,周风余,等.多PCA模型及SVM-DS融合决策的服务机器人故障诊断[J].振动、测试与诊断,2015(3):434-440.YUAN Xianfeng,SONG Mumin,ZHOU Fengyu,et al.Service robot fault diagnosis based on multi-PCA model and SVM-DS fusion decision[J].Journal of Vibration,Measurement&Diagnosis,2015(3):434-440.
[9]靳行,林建辉,伍川辉,等.基于EEMD-TEO熵的高速列车轴承故障诊断方法[J].西南交通大学学报,2018,53(2):359-366.JIN Hang,LIN Jianhui,WU Chuanhui,et al.Diagnostic method for high-speed train bearing fault based on EEMD-TEO entropy[J].Journal of Southwest Jiaotong University,2018,53(2):359-366.
[10]LI Z,YAN X,TIAN Z,et al.Blind vibration component separation and nonlinear feature extraction applied to the nonstationary vibration signals for the gearbox multi-fault diagnosis[J].Measurement Journal of the International Measurement Confederation,2013,46(1):259-271.
[11]LOPEZ J E,TENNEY R R,DECKERT J C.Fault detection and identification using real-time wavelet feature extraction[C]∥IEEE-Sp International Symposium on Time-Frequency and Time-Scale Analysis.Philadelphia:IEEE,1994.
[12]YU D,CHENG J,YANG Y.Application of EMD method and Hilbert spectrum to the fault diagnosis of roller bearings[J].Mechanical Systems&Signal Processing,2005,19(2):259-270.
[13]WU Z,HUANG N E.Ensemble empirical mode decomposition:a noise-assisted data analysis method[J].Advances in Adaptive Data Analysis,2005,1(1):41.
[14]丁建明,林建辉,王晗,等.万向轴动不平衡检测的二代小波变换奇异值方法[J].机械工程学报,2014(12):110-117.DING Jianming,LIN Jianhui,WANG Han,et al.Detection of the dynamic unbalance with cardan shaft applying the second wavelet transform and singular value decomposition[J].Journal of Mechanical Engineering,2014(12):110-117.
[15]SWELDENS W.Lifting scheme:a new philosophy in biorthogonal wavelet constructions[C]∥Wavelet Applications in Signal and Image Processing III.San Diego:SIPE,1995.
[16]RICHMAN J S,MOORMAN J R.Physiological time-series analysis using approximate entropy and sample entropy[J].American Journal of Physiology-Heart and Circulatory Physiology,2000,278(6):2039-2049.
[17]王新沛,杨静,李远洋,等.基于样本熵快速算法的心音信号动力学分析[J].振动与冲击,2010,29(11):115-118.WANG Xinpei,YANG Jing,LI Yuanyang,et al.Dynamic analysis of heart sound signal with a sample entropy fast algorithm[J].Journal of Vibration and Shock,2010,29(11):115-118.