Wind Turbine Gearbox Fault Diagnosis Method Based on Wavelet Packet and SVM
Abstract: In order to monitor gearbox real-timely, which is the core component of wind turbine, a method is put forward. This method is based on some vibration signals that are caused by different parts of gearbox at work and common faults of gearbox. Firstly, a gearbox working signal acquiring system is created. It uses high precision sensors to acquire signals when the wind turbine gearbox is working. Secondly, according to features of vibration signals of gearbox at work, using wavelet packet transform method can extract characteristics from working signals. Sending those data to Support Vector Machine (SVM), the system can implement intelligent fault diagnosis. At last, through experiments under laboratory condition, this method can reach more than 97.5% classification accuracy in the case of small sample.
文章引用: 欧淇源 , 姚为星 , 周求湛 , 程文阁 , 吴艳茹 (2013) 基于小波包和SVM的风机齿轮箱故障诊断方法。 声学与振动， 1， 37-43. doi: 10.12677/OJAV.2013.14006
 Ma, C.-L., Duan, B., et al. (2011) Design and Application of Wind Farm Fault Diagnosis System. Power and Energy Engineering Conference (APPEEC). 2011 Asia-Pacific, Wuhan, 2528 March 2011, 1-4.
 Zheng, X.-X., Xu, H.-S., et al. (2012) Fault Diagnosis of Wind Turbine Rolling Bearing Based on Wavelet and Hilbert Transforms. Control Conference (CCC), 2012 31st Chinese, Heifei, 25-27 July 2012, 24-26.
 Shen, C.-Q., Dong, W., et al. (2013) Fault Diagnosis of Rotating Machinery Based on the Statistical Parameters of Wavelet Packet Paving and a Generic Support Vector Regressive Classifier. Measurement. Journal of the International Measurement Confederation, 46, 1551-1564.
 Corinna, C. and Vladimir, V. (1995) Support-Vector Network. Machine Learning, 20, 273-297.
 Rafiee, J., Arvani, F., Harifi, S., et al. (2007) Intelligent Condition Monitoring of a Gearbox Using Artificial Neural Network. Mechanical Systems and Signal Processing, 21, 1746-1754.
 Lai, W.-X., Zhang, G.-C., et al. (2004) Classification of Gear Faults Using Cumulates and the Radial Basis Function Network. Mechanical Systems and Signal Processing, 18, 381-389.
 Sheng, X.-L., Wan, S.-T., et al. (2011) Gear Fault Diagnosis of Wind Turbine Generator System Based on Lifting WaveletZooming Envelope Analysis. Proceedings of the 2011 2nd International Conference on Mechanic Automation and Control Engineering, Hohhot, 15-17 July 2011, 1332-1335.
 Chen, J., Xue, T.-W. and Jing, J.-L. (2011) Gear Fault Diagnosis Based on Harmonic Wavelet Packet and Bp Neural Network. Proceedings of the 2nd International Conference on Advanced Design and Manufacturing Engineering, Taiyuan, 9-11 November 2011, 2683-2687.
 Peng, H., Wen, Y.-X., et al. (2009) Crack Detection in Eggs with Multi-Level Wavelet Transform and Bp Neural Network. Nongye Jixie Xuebao/Transactions of the Chinese Society of Agricultural Machinery, 40, 170-174.
 Jiang, Y.-H., Tang, B.-P., et al. (2011) Feature Extraction Method of Wind Turbine Based on Adaptive Morlet Wavelet and SVD. Renewable Energy, 36, 2146-2153.
 Hu, Q., He, Z.-J., et al. (2007) Diagnosis of Rotating Machinery Based on Improved Wavelet Package Transform and SVM Ensemble. Mechanical System and Signal Processing, 21, 688-705.
 Yang, B.-S., Hwang, W.-W., et al. (2005) Cavitation Detection of Butterﬂy Valve Using Support Vector Machines. Journal of Sound Vibration, 287, 25-43.
 Yuan, S.-F. and Chu, F.-L. (2006) Support Vector MachinesBased Fault Diagnosis for Turbo-Pump Rotor. Mechanical System and Signal Processing, 20, 939-952.
 Zhang, Z.-S., Hu, Q., et al. (2005) Fuzzy Support Vector Machine and Its Application to Mechanical Condition Monitoring. Chongqing, 30 May-1 June 2005, 937-942.
 Choi, K.H., Namburu, S.M., Azam, M.S., Luo, J., Pattipati, K.R. and Hine, A.P. (2005) Fault Diagnosis in HVAC Chillers. IEEE Instrumentation& Measurement Magazine, 8, 24-32.
 Ge, M., Du, R., et al. (2004) Fault Diagnosis Using Support Vector Machine with an Application in Sheet Metal Stamping Operations. Me-chanical System and Signal Processing, 18, 143159.