Abstract:When the induction motor fails, the fault signals are generally weak and in low frequency. If the fault couples into a composite one,nonlinear coupling between the signals will make spectrum more complicated. For above-mentioned problem, a new method is proposed, which combines the spectral kurtosis and Hilbert envelope demodulation. Firstly, Hilbert transformation is applied to obtain fault features including low-frequency envelope signal. It can highlight and separate the fault frequency components. Secondly, band pass filter whose parameters is determined by the principle of maximizing kurtosis is used to deal with the noise in signal. And then fault frequency components can be found by squared envelope spectrum. The paper also analyzes the influences of different faults vary with the changes of the load. Experimental and actual results show that this method is adaptive and efficient in single and composite faults in the cases of various noise levels and different loads.
赵妍, 李志民, 李天云. 一种基于谱峭度的异步电机故障诊断方法[J]. 电工技术学报, 2014, 29(5): 189-196.
Zhao Yan, Li Zhimin, Li Tianyun. A Method for Fault Diagnosis of Induction Motors Based on Spectral Kurtosis. Transactions of China Electrotechnical Society, 2014, 29(5): 189-196.
[1] IAS Motor Reliability Working Group. Report of large motor reliability survey of industrial and commercial installations, part I-II[J]. IEEE Transactions on Industry Applications, 1985, 21(4): 853-872. [2] IAS Motor Reliability Working Group. Report of large motor reliability survey of industrial and commercial installations, part III[J]. IEEE Transactions on Industry Applications, 1987, 23(1): 153-158. [3] Thorsen O V, Dalva M. A survey of faults on induction motors in offshore oil industry, petrochemical industry, gas terminals, and oil refineries[J]. IEEE Transactions on Industry Applications, 1995, 31(5): 1186-1196. [4] 任震, 张征平, 黄雯莹, 等. 基于最优小波包基的电机故障信号的消噪与检测[J]. 中国电机工程学报, 2002, 22(8): 53-57. Ren Zhen, Zhang Zhengping, Huang Wenying, et al. Denosing and detection of faulted motor signal based on best wavelet packet basis[J]. Proceedings of the CSEE, 2002, 22(8): 53-57. [5] 侯新国, 吴正国, 夏立. 基于Park 矢量模平方函数的异步电机转子故障检测方法研究[J]. 中国电机工程学报, 2003, 23(9): 137-140. Hou Xinguo, Wu Zhengguo, Xia Li. A method for detecting rotor faults in asynchronous motors based on the square of the Park’s vector modulus[J]. Proceedings of the CSEE, 2003, 23(9): 137-140. [6] 刘振兴, 尹项根, 张哲. 基于Hilbert 模量频谱分析的异步电机转子故障在线监测与诊断方法[J]. 中国电机工程学报, 2003, 23 (7): 158- 161. Liu Zhenxing, Yin Xianggen, Zhang Zhe. Online monitoring and diagnosis way based on spectrum analysis of Hilbert modulus in induction motors[J]. Proceedings of the CSEE, 2003, 23 (7): 158 -161. [7] Cruz S M, Marques A J. Rotor cage fault diagnosis in three-phase induction motors by extended Park’s vector approach[J]. Electric Machines and Power Systems, 2000, 28(5): 289-299. [8] 冷永刚, 王太勇, 李瑞欣, 等. 变尺度随机共振用于电机故障的监测诊断. 中国电机工程学报, 2003, 23(11): 111-115. Leng Yonggang, Wang Taiyong, Li Ruixin, et al. Scale transformation stochastic resonance for the monitoring and diagnosis of electromotor faults[J]. Proceedings of the CSEE, 2003, 23(11): 111-115. [9] 李天云, 李光, 杨春玲, 等. 基于自适应随机共振的异步电机转子断条故障检测. 中国电机工程学报, 2007, 27(15): 88-92. Li Tianyun, Li Guang, Yang Chunling, et al. New approach of broken rotor bar detection in induction motor based on adaptive stochastic resonance[J]. Proceedings of the CSEE, 2007, 27(15): 88-92. [10] 张希熊, 刘振兴. 基于倒频谱分析的电机故障检测[J]. 电力系统保护与控制, 2010, 38(20): 145-147. Zhang Xixiong, Liu Zhenxing. Fault detection for motor based on cepstrum analysis[J]. Power System Protection and Control, 2010, 38(20): 145-147. [11] 张含蕾, 周洁敏, 李刚. 基于小波分析的感应电机复合故障诊断[J]. 中国电机工程学报, 2006, 26(8): 159-162. Zhang Hanlei, Zhou Jiemin, Li Gang. Mixed fault diagnosis based on wavelet analysis in induction motors[J]. Proceedings of the CSEE, 2006, 26(8): 159-162. [12] Dwyer R F. Detection of non-gaussian signals by frequency domain kurtosis estimation[C]. Int. Conference on Acoustic, Speech and Signal Processing, 1983: 607-610. [13] Antoni J. The spectral kurtosis: a useful tool for characterising non-stationary signals[J]. Mechanical Systems and Signal Processing, 2006, 20(2): 282- 307. [14] Antoni J, Randall R B. The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines[J]. Mechanical Systems and Signal Processing, 2006, 20: 308-331. [15] Antoni J. Fast computation of the kurtogram for the detection of transient faults[J]. Mechanical Systems and Signal Processing, 2007, 21: 108-124. [16] Tomasz Barszcz, Robert B Randall. Application of spectral kurtosis for detection of a tooth crack in the planetary gear of a wind turbine[J]. Mechanical Systems and Signal Processing, 2009, 23: 1352-1365. [17] 苏文胜, 王奉涛, 张志新, 等. EMD降噪和谱峭度法在滚动轴承早期故障诊断中的应用[J]. 振动与冲击, 2010, 29(3): 18-22. Su Wensheng, Wang Fengtao, Zhang Zhixin, et al. Application of EMD denoising and spectral kurtosis in early fault diagnosis of rolling element bearings[J]. Journal of Vibration and Shock, 2010, 29(3): 18-22. [18] 王晓冬, 何正嘉, 訾艳阳. 滚动轴承故障诊断的多小波谱峭度方法[J]. 西安交通大学学报, 2010, 44(3): 77-81. Wang Xiaodong, He Zhengjia, Zi Yanyang. Spectral kurtosis of multiwavelet for fault diagnosis of rolling bearing[J]. Journal of Xi’an Jiaotong University, 2010, 44(3): 77-81. [19] 沈金伟, 石林锁. 滚动轴承故障诊断的改进小波变换谱峭度法[J]. 轴承, 2010, 8(1): 46-49. Shen Jinwei, Shi Linsuo. Improved spectral kurtosis algorithm based on wavelet transformation for rolling bearing fault diagnosis[J]. Bearing, 2010, 8(1): 46-49. [20] 石林锁, 张亚洲, 米文鹏. 基于WVD的谱峭度法在轴承故障诊断中的应用[J]. 震动、测试与诊断, 2011, 31(1): 27-33. Shi Linsuo, Zhang Yazhou, Mi Wenpeng. Application of wigner-ville-distribution-based spectral kurtosis algorithm to fault diagnosis of rolling bearing[J]. Journal of Vibration, Measurement & Diagnosis, 2011, 31(1): 27-33. [21] Fabien Millioz, Nadine Martin. Circularity of the STFT and spectral kurtosis for time-frequency segmentation[J]. Transactions on Signal Processing, 2011, 59(2): 515 -525. [22] Wang Yanxue, Liang Ming. An adaptive SK technique and its application for fault detection of rolling element bearings[J]. Mechanical Systems and Signal Processing, 2011, 25: 750-764.