Bearing Fault Diagnosis Method Based on EEMD and LSTM

  • Ping Zou School of Economics and Management,Beijing Jiaotong University
  • Baocun Hou
  • Jiang Lei Railway Information Center,China National Railway Corporation
  • Zhenji Zhang School of Economics and Management,Beijing Jiaotong University


The 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.

Author Biography

Ping Zou, School of Economics and Management,Beijing Jiaotong University
Product R & D department Director, Beijing Aerospace Intelligent Manufacturing Technology Development Co., Ltd


[1] Alangari, H.; Kimura, Y. (2017). A Hybrid EMD-Kurtosis Method for Estimating Fetal Heart Rate from Continuous Doppler Signals, physiology, 8, 2017.

[2] Andrychowicz, M.; Denil, M. (2016). Learning to learn by gradient descent by gradient descent, Proc. NIPS, 2016.

[3] Auli, M.; Galley, M.; Quirk, C.; Zweig, G. (2013). Joint Language and Translation Modeling with Recurrent Neural Networks, Proc. of EMNLP, 2013.

[4] Bahdanau, D.; Cho, K.; Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate, arXiv preprint arXiv, 1409.0473, 2014.

[5] Bengio, Y. (2009). Learning deep architectures for ai, Found, Trends Mach. Learn, 2(1), 1-127, 2009.

[6] 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.

[7] Blitzer, J.; McDonald, R.; Pereira, F. (2006). Domain adaptation with structural correspondence learning, Proceedings of EMNLP, 120-128, 2006.

[8] Chu, X.; Liu, J.; Gong, D.; Wang, R. (2019). Preserving Location Privacy in Spatial Crowdsourcing under Quality Control, IEEE Access, 7, 155851-155859, 2019.

[9] 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.

[10] 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.

[11] 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.

[12] Gu, N.L.; , Pan, H. (2017). Bearing Fault Diagnosis Method Based on EMD-CNNs, CSMA, 2017.

[13] 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.

[14] Hinton, G.E.; Salakhutdinov, R.R.(2006). Reducing the dimensionality of data with neural networks, Science, 313(5786), 504-507, 2006.

[15] 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.

[16] 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.

[17] 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.

[18] 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.

[19] Liu, R.; Yang, B. (2018). Artificial intelligence for fault diagnosis of rotating machinery, Mech. Syst. Signal Process, 135, 33-47, 2018.

[20] 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.

[21] Randall, R.B.; Antoni, J.(2011). Rolling element bearing diagnostics-A tutorial, Mech. Syst. Signal Process, 25, 485-520, 2011.

[22] 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.

[23] Tabsh, Y.; Davidaviciene, V. (2016). Information and Communication Technologies in Energy Management, Journal of System and Management Science, 6(4), 67-81, 2016.

[24] Wang, D.; Tsui, K. (2018). Brownian motion with adaptive drift for remaining useful life prediction: revisited, Mech. Syst. Signal Process, 99, 691-701, 2018.

[25] Wang, J.; Du, G. (2020). Fault diagnosis of rotating machines based on the EMD manifold, Mech. Syst. Signal Process, 135, 106443, 2020.

[26] 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.

[27] Wu, C.; Jiang, P. (2019). Intelligent fault diagnosis of rotating machinery based on onedimensional convolutional neural network, Computers in Industry, 108, 53-61, 2019.

[28] 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.

[29] Xu, X.; Chen, R. (2007). Recurrent Neural Network Based On-line Fault Diagnosis Approach for Power Electronic Devices, ICNC, 24-27, 2007.

[30] Yink, W.; Kann, K.(2017). Comparative Study of CNN and RNN for Natural Language Processing, Computer Science, 1702.01923, 2017.

[31] Zhao, H.; Sun, S. (2016). Sequential Fault Diagnosis based on LSTM Neural Network, IEEE Access, 6, 12929-12939, 2018.

[32] 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.

[33] [Online]. Available:, Accesed on 14 February 2019.
How to Cite
ZOU, Ping et al. Bearing Fault Diagnosis Method Based on EEMD and LSTM. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 15, n. 1, feb. 2020. ISSN 1841-9844. Available at: <>. Date accessed: 01 dec. 2020. doi:


fault diagnosis, EEMD, LSTM, motor bearing.