Weitere Artikel dieser Ausgabe durch Wischen aufrufen
A novel methodology for early diagnosis of rolling element bearing fault is employed based on continuous wavelet transform (CWT) and support vector machine (SVM). CWT is especially suited for analyzing non-stationary signals in time–frequency domain where time information is retained as well as frequency content. To better approximate non-stationary vibration signals from rolling element bearing, a wavelet choice criterion is established to select an appropriate mother wavelet for feature extraction. The Shannon wavelet is picked out of several considered wavelets. The classification tree kernels (CTK) are constructed to address nonlinear classification of the characteristic samples derived from the wavelet coefficients. By using Fuzzy pruning strategy, a large variety of classification trees are generated. The trees with diverse structures can effectively explore intrinsic information among samples. Then, the tree kernel matrices can be acquired through ensemble statistical learning, which eventually reveal the similarity of samples objectively and stably. Under such architecture of kernel methods, a classification tree kernel based support vector machine (CTKSVM) is proposed to identify bearing fault. The performance of the methodology involving CWT and CTKSVM (CWT–CTKSVM) is evaluated by cross validation and independent test. The results show that the CWT–CTKSVM totally is superior to other SVM methods with common kernels. Therefore, it is a prospective technique for detection and identification of rolling element bearing fault.
Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten
Sie möchten Zugang zu diesem Inhalt erhalten? Dann informieren Sie sich jetzt über unsere Produkte:
Abbasion, S., Rafsanjani, A., Farshidianfar, A., & Irani, N. (2007). Rolling element bearings multi-fault classification based on the wavelet denoising and support vector machine. Mechanical Systems and Signal Processing, 21, 2933–2945. CrossRef
Ebrahimipour, V., Najjarbashi, A., & Sheikhalishahi, M. (2015). Multi-objective modeling for preventive maintenance scheduling in a multiple production line. Journal of Intelligent Manufacturing, 26(1), 111–122. CrossRef
Finley, W. R., Hodwanec, M. M., & Holter, W. G. (2000). An analytical approach to solving motor vibration problems. IEEE Transactions on Industry Applications, 36(5), 1467–1480. CrossRef
Gryllias, K. C., & Antoniadis, I. A. (2012). A Support Vector Machine approach based on physical model training for rolling element bearing fault detection in industrial environments. Engineering Applications of Artificial Intelligence, 25, 326–344. CrossRef
He, Q., Wang, J., Liu, Y., Dai, D., & Kong, F. (2012). Identification of bearing faults using time domain zero-crossings. Mechanical Systems and Signal Processing, 28, 443–457. CrossRef
Hu, N., Chen, M., & Wen, X. (2003). The application of stochastic esonance theory for early detecting rub-impacting fault of rotor. Mechanical Systems and Signal Processing, 17(4), 883–895. CrossRef
Hu, Q., He, Z., Zhang, Z., & Zi, Y. (2007). Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMs ensemble. Mechanical Systems and Signal Processing, 21, 688–705. CrossRef
Immovilli, F., Cocconcelli, M., Bellini, A., & Rubini, R. (2009). Detection of generalized roughness bearing fault by spectral kurtosis energy of vibration or current signals. IEEE Transactions on Industrial Electronics, 56, 4710–4717. CrossRef
Irfan, M., Saad, N., Ibrahim, R., & Asirvadam, V. S. (2015). Condition monitoring of induction motors via instantaneous power analysis. Journal of Intelligent Manufacturing. doi: 10.1007/s10845-015-1048-2.
Junsheng, C., Dejie, Y., & Yu, Y. (2006). Research on intrinsic mode function (IMF) criterion in EMD method. Mechanical Systems and Signal Processing, 20, 817–824. CrossRef
Junsheng, C., Dejie, Y., & Yu, Y. (2006). A fault diagnosis approach for roller bearings based on EMD method and AR model. Mechanical Systems and Signal Processing, 20(2), 350–362. CrossRef
Kankar, P. K., Sharma, S. C., & Harsha, S. P. (2011). Fault diagnosis of ball bearings using continuous wavelet transform. Applied Soft Computing, 11, 2300–2312. CrossRef
Konar, P., & Chattopadhyay, P. (2011). Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs). Applied Soft Computing, 11, 4203–4211. CrossRef
Li, B., Chow, M. Y., Tipsuwan, Y., & Hung, J. C. (2000). Neural network based motor rolling bearing fault diagnosis. IEEE Transactions on Industrial Electronics, 47, 1060–1069. CrossRef
Li, H., Lian, X., Guo, C., & Zhao, P. (2015). Investigation on early fault classification for rolling element bearing based on the optimal frequency band determination. Journal of Intelligent Manufacturing, 26(1), 189–198. CrossRef
Liu, S., Hu, Y., Li, C., Lu, H., & Zhang, H. (2015). Machinery condition prediction based on wavelet and support vector machine. Journal of Intelligent Manufacturing. doi: 10.1007/s10845-015-1045-5.
Liu, B., Riemenschneider, S., & Xu, Y. (2006). Gearbox fault diagnosis using empirical mode decomposition and Hilbert spectrum. Mechanical Systems and Signal Processing, 20, 718–734. CrossRef
Lou, X., & Loparo, K. A. (2004). Bearing fault diagnosis based on wavelet transform and fuzzy inference. Mechanical Systems and Signal Processing, 18, 1077–1095. CrossRef
Nikolaou, N. G., & Antoniadis, I. A. (2002). Rolling element bearing fault diagnosis using wavelet packets. NDT&E International, 35, 197–205. CrossRef
Peng, Z. K., Tse, P. W., & Chu, F. L. (2005). An improved Hilbert–Huang transform and its application in vibration signal analysis. Journal of Sound and Vibration, 286, 187–205. CrossRef
Radhika, S., Sabareesh, G. R., Jaganand, G., & Sugumaran, V. (2010). Precise wavelet for current signature in 3ø IM. Expert Systems with Applications, 37, 450–455. CrossRef
Rafiee, J., Tse, P. W., Harifi, A., & Sadeghi, M. H. (2009). A novel technique for selecting mother wavelet function using an intelligent fault diagnosis system. Expert Systems with Applications, 36, 4862–4875. CrossRef
Rai, V. K., & Mohanty, A. R. (2007). Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert–Huang transform. Mechanical Systems and Signal Processing, 21, 2607–2615.
Randall, R. B. (1982). A new method of modeling gear faults. ASME Journal of Mechanical Design, 104(2), 259–267. CrossRef
Saimurugan, M., Ramachandran, K. I., Sugumaran, V., & Sakthivel, N. R. (2011). Multi component fault diagnosis of rotational mechanical system based on decision tree and support vector machine. Expert Systems with Applications, 38, 3819–3826. CrossRef
Samanta, B., & Al-Balushi, K. R. (2003). Artificial neural network based fault diagnostics of rolling element bearings using time-domain features. Mechanical Systems and Signal Processing, 17(2), 317–328. CrossRef
Saravanan, N., Kumar Siddabattuni, V. N. S., & Ramachandran, K. I. (2010). Fault diagnosis of spur bevel gear box using artificial neural network (ANN) and proximal support vector machine (PSVM). Applied Soft Computing, 10, 344–360. CrossRef
Saucedo-Espinosa, M. A., Escalante, H. J., & Berrones, A. (2014). Detection of defective embedded bearings by sound analysis: a machine learning approach. Journal of Intelligent Manufacturing. doi: 10.1007/s10845-014-1000-x.
Saxena, A., & Saad, A. (2007). Evolving an artificial neural network classifier for condition monitoring of rotating mechanical systems. Applied Soft Computing, 7, 441–454. CrossRef
Seera, M., Lim, C. P., & Loo, C. K. (2014). Motor fault detection and diagnosis using a hybrid FMM–CART model with online learning. Journal of Intelligent Manufacturing. doi: 10.1007/s10845-014-0950-3.
Sugumaran, V., & Ramachandran, K. I. (2011). Effect of number of features on classification of roller bearing faults using SVM and PSVM. Expert Systems with Applications, 38, 4088–4096. CrossRef
Tan, J., et al. (2009). Study of frequency-shifted and re-scaling stochastic resonance and its application to fault diagnosis. Mechanical Systems and Signal Processing, 23(3), 811–822. CrossRef
Tsao, W., Li, Y., Le, D., & Pan, M. (2012). An insight concept to select appropriate IMFs for envelop analysis of bearing fault diagnosis. Measurement, 45, 1489–1498. CrossRef
Wanga, C., Kang, Y., Shen, P., Chang, Y., & Chung, Y. (2010). Applications of fault diagnosis in rotating machinery by using time series analysis with neural network. Expert Systems with Applications, 37, 1696–1702. CrossRef
William, P. E., & Hoffman, M. W. (2011). Identification of bearing faults using time domain zero-crossings. Mechanical Systems and Signal Processing, 25, 3078–3088. CrossRef
Xian, G. (2010). Mechanical failure classification for spherical roller bearing of hydraulic injection molding machine using DWT–SVM. Expert Systems with Applications, 37, 6742–6747. CrossRef
Yan, R., & Gao, R. X. (2008). Rotary machine health diagnosis based on empirical mode decomposition. Journal of Vibration and Acoustics, 130(2), 021007. CrossRef
Yu, D., Cheng, J., & Yang, Y. (2005). Application of EMD method and Hilbert spectrum to the fault diagnosis roller bearings. Mechanical Systems and Signal Processing, 19(2), 259–270. CrossRef
Zare, J. (2012). Induction motor bearing fault detection using pattern recognition techniques. Expert Systems with Applications, 39, 68–73. CrossRef
Ziani, R., Felkaoui, A., & Zegadi, R. (2014). Bearing fault diagnosis using multiclass support vector machines with binary particle swarm optimization and regularized Fisher’s criterion. Journal of Intelligent Manufacturing. doi: 10.1007/s10845-014-0987-3.
- A novel approach to wavelet selection and tree kernel construction for diagnosis of rolling element bearing fault
- Springer US
in-adhesives, MKVS, Hellmich GmbH/© Hellmich GmbH, Neuer Inhalt/© momius | stock.adobe.com