Bearing Fault Diagnosis Method Based on EEMD and LSTM
Keywords:fault diagnosis, EEMD, LSTM, motor bearing.
AbstractThe condition monitoring and fault detection of rolling bearing are of great significance to ensure the safe and reliable operation of rotating machinery system.In the past few years, deep neural network (DNN) has been recognized as an effective tool to detect rolling bearing faults. However, It is too complex to directly feed the original vibration signal to the DNN neural network, and the accuracy of fault identification is not high. By using the signal preprocessing technology, the original signal can be effectively removed and preprocessed without losing the key diagnosis information. In this paper, a new EEMD-LSTM bearing fault diagnosis method is proposed, which combines the signal preprocessing technology with the EEMD method that can get clear fault feature signals, and LSTM technology to extract fault features automatically that improves the efficiency of fault feature extraction. In the case of small sample size, this method can significantly improve the accuracy of fault diagnosis.
Alangari, H.; Kimura, Y. (2017). A Hybrid EMD-Kurtosis Method for Estimating Fetal Heart Rate from Continuous Doppler Signals, physiology, 8, 2017. https://doi.org/10.3389/fphys.2017.00641
Andrychowicz, M.; Denil, M. (2016). Learning to learn by gradient descent by gradient descent, Proc. NIPS, 2016.
Auli, M.; Galley, M.; Quirk, C.; Zweig, G. (2013). Joint Language and Translation Modeling with Recurrent Neural Networks, Proc. of EMNLP, 2013.
Bahdanau, D.; Cho, K.; Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate, arXiv preprint arXiv, 1409.0473, 2014.
Bengio, Y. (2009). Learning deep architectures for ai, Found, Trends Mach. Learn, 2(1), 1-127, 2009. https://doi.org/10.1561/2200000006
Bengio, Y.; Courville, A.; Vincent, P.(2013). Representation learning: a review and new perspectives, IEEE Trans. Pattern Anal. Mach. Intell, 35(8), 1798-1828, 2013. https://doi.org/10.1109/TPAMI.2013.50
Blitzer, J.; McDonald, R.; Pereira, F. (2006). Domain adaptation with structural correspondence learning, Proceedings of EMNLP, 120-128, 2006. https://doi.org/10.3115/1610075.1610094
Chu, X.; Liu, J.; Gong, D.; Wang, R. (2019). Preserving Location Privacy in Spatial Crowdsourcing under Quality Control, IEEE Access, 7, 155851-155859, 2019. https://doi.org/10.1109/ACCESS.2019.2949409
Chu, X.; Zhong, Q.; Li, X. (2018). Reverse channel selection decisions with a joint third-party recycler, International Journal of Production Research, 56(18), 5969-5981, 2018. https://doi.org/10.1080/00207543.2018.1442944
Gong, D.; Tang, M.; Liu, S.; Xue, G.; Wang, L.(2019). Achieving sustainable transport through resource scheduling: A case study for electric vehicle charging stations, Advances in Production Engineering & Management, 14(1), 65-79, 2019. https://doi.org/10.14743/apem2019.1.312
Gong, D.; Liu, S.; Liu, J.; Ren, L.(2019). Who benefits from online financing? A sharing economy E-tailing platform perspective, International Journal of Production Economics, DOI: 10.1016/j.ijpe.2019.09.011. https://doi.org/10.1016/j.ijpe.2019.09.011
Gu, N.L.; , Pan, H. (2017). Bearing Fault Diagnosis Method Based on EMD-CNNs, CSMA, 2017. https://doi.org/10.12783/dtcse/csma2017/17383
He, Q.; Li, P.; Kong, F. (2012). Rolling bearing localized defect evaluation by multiscale signature via empirical mode decomposition, J. Vib. Acoust, 134, 061013, 2012. https://doi.org/10.1115/1.4006754
Hinton, G.E.; Salakhutdinov, R.R.(2006). Reducing the dimensionality of data with neural networks, Science, 313(5786), 504-507, 2006. https://doi.org/10.1126/science.1127647
Huang, N.E.; Shen, Z.; Long, S.; Wu, M.; Shih, H.; Zheng, Q.; Yen, N.-C.; Tung, C.; Liu, H. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis, Proc. R. Soc. A Math. Phys. Eng. Sci, 454, 903-995, 1998. https://doi.org/10.1098/rspa.1998.0193
Karpathy, A.; Fei-Fei, L. (2015). Deep visual-semantic alignments for generating image descriptions, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3128-3137, 2015. https://doi.org/10.1109/CVPR.2015.7298932
Lei, Y.; He, Z.(2009). Application of the EEMD method to rotor fault diagnosis of rotating machinery, Mechanical Systems and Signal Processing, 23(4), 1327-1338, 2009. https://doi.org/10.1016/j.ymssp.2008.11.005
Lei, Y.; He, Z. (2011). EEMD method and WNN for fault diagnosis of locomotive roller bearings, Expert Systems with Applications, 38(6), 7334-7341, 2011. https://doi.org/10.1016/j.eswa.2010.12.095
Liu, R.; Yang, B. (2018). Artificial intelligence for fault diagnosis of rotating machinery, Mech. Syst. Signal Process, 135, 33-47, 2018. https://doi.org/10.1016/j.ymssp.2018.02.016
Nazifa, T.S.; Mohaed, S.F.; Amin, A.B. (2019). A Brief Discussion on Supply Chain Management in Construction Industry, Journal of System and Management Science, 9(1), 69-86, 2019.
Randall, R.B.; Antoni, J.(2011). Rolling element bearing diagnostics-A tutorial, Mech. Syst. Signal Process, 25, 485-520, 2011. https://doi.org/10.1016/j.ymssp.2010.07.017
Song, L.; Wang, H.; Chen, P. (2018). Vibration-Based Intelligent Fault Diagnosis for Roller Bearings in Low-Speed Rotating Machinery, IEEE Trans. Instrum. Meas, 67, 1887-1899, 2018. https://doi.org/10.1109/TIM.2018.2806984
Tabsh, Y.; Davidaviciene, V. (2016). Information and Communication Technologies in Energy Management, Journal of System and Management Science, 6(4), 67-81, 2016.
Wang, D.; Tsui, K. (2018). Brownian motion with adaptive drift for remaining useful life prediction: revisited, Mech. Syst. Signal Process, 99, 691-701, 2018. https://doi.org/10.1016/j.ymssp.2017.07.015
Wang, J.; Du, G. (2020). Fault diagnosis of rotating machines based on the EMD manifold, Mech. Syst. Signal Process, 135, 106443, 2020. https://doi.org/10.1016/j.ymssp.2019.106443
Wang, T.; Zhang, M. (2012). Comparing the applications of EMD and EEMD on time-frequency analysis of seismic signal, Journal of Applied Geophysics, 83, 29-34, 2012. https://doi.org/10.1016/j.jappgeo.2012.05.002
Wu, C.; Jiang, P. (2019). Intelligent fault diagnosis of rotating machinery based on onedimensional convolutional neural network, Computers in Industry, 108, 53-61, 2019. https://doi.org/10.1016/j.compind.2018.12.001
Wu, Z.; Huang, N.E. (2006). Ensemble empirical mode decomposition:a noise assisteted data analysis method, Advances in Adaptive Data Analysis, 1(1), 1-41, 2009. https://doi.org/10.1142/S1793536909000047
Xu, X.; Chen, R. (2007). Recurrent Neural Network Based On-line Fault Diagnosis Approach for Power Electronic Devices, ICNC, 24-27, 2007. https://doi.org/10.1109/ICNC.2007.599
Yink, W.; Kann, K.(2017). Comparative Study of CNN and RNN for Natural Language Processing, Computer Science, 1702.01923, 2017.
Zhao, H.; Sun, S. (2016). Sequential Fault Diagnosis based on LSTM Neural Network, IEEE Access, 6, 12929-12939, 2018. https://doi.org/10.1109/ACCESS.2018.2794765
Zhao, R.; Wang, J.; Yan, R.; Mao, K. (2016). Machine health monitoring with LSTM networks, Proceedings of the 2016 10th International Conference on Sensing Technology (ICST), 1-6, 2016. https://doi.org/10.1109/ICSensT.2016.7796266
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