Stacked Auto-encoder Based Feature Transfer Learning and Optimized LSSVM-PSO Classifier in Bearing Fault Diagnosis

[1] Chen, Z., Deng, S., Chen, X., Li, C., Sanchez, R.-V. Qin, H. (2017). Deep neural networks-based rolling bearing fault diagnosis. Microelectronics Reliability, 75, 327-333. https://doi.org/10.1016/j.microrel.2017.03.00610.1016/j.microrel.2017.03.006 Search in Google Scholar

[2] Hong, L., Dhupia, J.S. (2014). A time domain approach to diagnose gearbox fault based on measured vibration signals. Journal of Sound and Vibration, 333 (7), 2164-2180. https://doi.org/10.1016/j.jsv.2013.11.03310.1016/j.jsv.2013.11.033 Search in Google Scholar

[3] Wang, X., Zheng, Y., Zhao, Z., Wang, J. (2015). Bearing fault diagnosis based on statistical locally linear embedding. Sensors, 15 (7), 16225-16247. https://doi.org/10.3390/s15071622510.3390/s150716225454187626153771 Search in Google Scholar

[4] 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 (5), 1060-1069. https://doi.org/10.1109/41.87321410.1109/41.873214 Search in Google Scholar

[5] Cheng, J., Yu, D., Yang, Y. (2006). A fault diagnosis approach for roller bearing based on EMD method and AR model. Mechanical Systems and Signal Processing, 20 (2), 350-362. https://doi.org/10.1016/j.ymssp.2004.11.00210.1016/j.ymssp.2004.11.002 Search in Google Scholar

[6] Cheng, J., Zheng, J., Yang, Y. (2012). A nonstationary signal analysis approach - The local characteristic-scale decomposition method. Zhendong Gongcheng Xuebao/Journal of Vibration Engineering, 25 (2), 215, DOI: 10.16385/j.cnki.issn.1004-4523.2012.02.002. Search in Google Scholar

[7] Cheng, J., Yang, Y., Yang, Y. (2012). A rotating machinery fault diagnosis method based on local mean decomposition. Digital Signal Processing, 22 (2), 356-366. https://doi.org/10.1016/j.dsp.2011.09.00810.1016/j.dsp.2011.09.008 Search in Google Scholar

[8] Lei, Y., Lin, J., He, Z., Zuo, M.J. (2013). A review on empirical mode decomposition in fault diagnosis of rotating machinery. Mechanical Systems and Signal Processing, 35 (1-2), 108-126. https://doi.org/10.1016/j.ymssp.2012.09.01510.1016/j.ymssp.2012.09.015 Search in Google Scholar

[9] Nguyen, V.H., Cheng, J.S., Yu, Y., Thai, V.T. (2019). An architecture of deep learning network based on ensemble empirical mode decomposition in precise identification of bearing vibration signal. Journal of Mechanical Science and Technology, 33, 41-50. https://doi.org/10.1007/s12206-018-1205-610.1007/s12206-018-1205-6 Search in Google Scholar

[10] Martinez, A.M., Kak, A.C. (2001). PCA versus LDA. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23 (2), 228-233. https://doi.org/10.1109/34.90897410.1109/34.908974 Search in Google Scholar

[11] Prieto-Moreno, A., Llanes-Santiago, O., Garcia-Moreno, E. (2015). Principal components selection for dimensionality reduction using discriminant information applied to fault diagnosis. Journal of Process Control, 33, 14-24. https://doi.org/10.1016/j.jprocont.2015.06.00310.1016/j.jprocont.2015.06.003 Search in Google Scholar

[12] Nguyen, V.H., Cheng, J.S., Thai, V.T. (2017). An integrated generalized discriminant analysis method and chemical reaction support vector machine model (GDA-CRSVM) for bearing fault diagnosis. Advances in Production Engineering & Management, 12 (4), 321-336. https://doi.org/10.14743/apem2017.4.26110.14743/apem2017.4.261 Search in Google Scholar

[13] Zhang, L., Zhang, Q., Zhang, L., Tao, D., Huang, X., Du, B. (2015). Ensemble manifold regularized sparse low-rank approximation for multiview feature embedding. Pattern Recognition, 48 (10), 3102-3112. https://doi.org/10.1016/j.patcog.2014.12.01610.1016/j.patcog.2014.12.016 Search in Google Scholar

[14] Yao, B., Zhen, P., Wu, L., Guan, Y. (2017). Rolling element bearing fault diagnosis using improved manifold learning. IEEE Access, 5, 6027-6035. https://doi.org/10.1109/ACCESS.2017.269337910.1109/ACCESS.2017.2693379 Search in Google Scholar

[15] Bengio, Y. (2009). Learning deep architectures for AI. Foundations and Trends® in Machine Learning, 2 (1), 1-127. http://dx.doi.org/10.1561/220000000610.1561/2200000006 Search in Google Scholar

[16] Hou, P., Wen, C., Dong, D. (2017). Rolling bearing fault diagnose based on stacked sparse auto encoder. In 2017 36th Chinese Control Conference (CCC). IEEE, 7027-7032. https://doi.org/10.23919/ChiCC.2017.8028463.10.23919/ChiCC.2017.8028463 Search in Google Scholar

[17] Bengio, Y., Courville, A., Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35 (8), 1798-1828. https://doi.org/10.1109/TPAMI.2013.5010.1109/TPAMI.2013.5023787338 Search in Google Scholar

[18] Bengio, Y. (2012). Deep learning of representations for unsupervised and transfer learning. JMLR: Workshop and Conference Proceedings, 27, 17-37. Search in Google Scholar

[19] Dou, D., Zhou, S. (2016). Comparison of four direct classification methods for intelligent fault diagnosis of rotating machinery. Applied Soft Computing, 46, 459-468. https://doi.org/10.1016/j.asoc.2016.05.01510.1016/j.asoc.2016.05.015 Search in Google Scholar

[20] Kankar, P.K., Sharma, S.C., Harsha, S.P. (2011). Fault diagnosis of ball bearings using machine learning methods. Expert Systems with Applications, 38 (3), 1876-1886. https://doi.org/10.1016/j.eswa.2010.07.11910.1016/j.eswa.2010.07.119 Search in Google Scholar

[21] Patel, J.P., Upadhyay, S.H. (2016). Comparison between artificial neural network and support vector method for a fault diagnostics in rolling element bearings. Procedia Engineering, 144, 390-397. https://doi.org/10.1016/j.proeng.2016.05.14810.1016/j.proeng.2016.05.148 Search in Google Scholar

[22] Yu, Y., YuDejie, Junsheng, C. (2006). A roller bearing fault diagnosis method based on EMD energy entropy and ANN. Journal of Sound and Vibration, 294 (1-2), 269-277. https://doi.org/10.1016/j.jsv.2005.11.00210.1016/j.jsv.2005.11.002 Search in Google Scholar

[23] Ben Ali, J., Fnaiech, N., Saidi, L., Chebel-Morello, B., Fnaiech, F. (2015). Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals. Applied Acoustics 89, 16-27. https://doi.org/10.1016/j.apacoust.2014.08.01610.1016/j.apacoust.2014.08.016 Search in Google Scholar

[24] Suykens, J.A.K., Vandewalle, J. (1999). Least squares support vector machine classifiers. Neural Processing Letters, 9, 293-300. https://doi.org/10.1023/A:101862860974210.1023/A:1018628609742 Search in Google Scholar

[25] Liu, X., Bo, L., Luo, H. (2015). Bearing faults diagnostics based on hybrid LS-SVM and EMD method. Measurement, 59, 145-166. https://doi.org/10.1016/j.measurement.2014.09.03710.1016/j.measurement.2014.09.037 Search in Google Scholar

[26] Zhang, Y., Qin, Y., Xing, Z., Jia, L., Cheng, X. (2013). Roller bearing safety region estimation and state identification based on LMD–PCA–LSSVM. Measurement, 46 (3), 1315-1324. https://doi.org/10.1016/j.measurement.2012.11.04810.1016/j.measurement.2012.11.048 Search in Google Scholar

[27] Nguyen, V., Hoang, T.D., Thai, V., Nguyen, X. (2019). Big vibration data diagnosis of bearing fault base on feature representation of autoencoder and optimal LSSVM-CRO classifier model. In 2019 International Conference on System Science and Engineering (ICSSE). IEEE, 557-563. https://doi.org/10.1109/ICSSE.2019.882333210.1109/ICSSE.2019.8823332 Search in Google Scholar

[28] Yunlong, Z., Peng, Z. (2012). Vibration fault diagnosis method of centrifugal pump based on EMD complexity feature and least square support vector machine. Energy Procedia, 17 (Part A), 939-945. https://doi.org/10.1016/j.egypro.2012.02.19110.1016/j.egypro.2012.02.191 Search in Google Scholar

[29] Su, Z., Tang, B., Liu, Z., Qin, Y. (2015). Multi-fault diagnosis for rotating machinery based on orthogonal supervised linear local tangent space alignment and least square support vector machine. Neurocomputing, 157, 208-222. https://doi.org/10.1016/j.neucom.2015.01.01610.1016/j.neucom.2015.01.016 Search in Google Scholar

[30] Budiman, A., Fanany, M.I., Basaruddin, C. (2014). Stacked Denoising Autoencoder for feature representation learning in pose-based action recognition. In 2014 IEEE 3rd Global Conference on Consumer Electronics (GCCE). IEEE, 684-688. https://doi.org/10.1109/GCCE.2014.703130210.1109/GCCE.2014.7031302 Search in Google Scholar

[31] Eberhart, R., Kennedy, J. (1995). A new optimizer using particle swarm theory. In Proceedings of the Sixth International Symposium on Micro Machine and Human Science (MHS ‘95). IEEE, 39-43. https://doi.org/10.1109/MHS.1995.49421510.1109/MHS.1995.494215 Search in Google Scholar