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End-to-end CNN + LSTM deep learning approach for bearing fault diagnosis

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Abstract

Fault diagnostics and prognostics are important topics both in practice and research. There is an intense pressure on industrial plants to continue reducing unscheduled downtime, performance degradation, and safety hazards, which requires detecting and recovering potential faults in its early stages. Intelligent fault diagnosis is a promising tool due to its ability to rapidly and efficiently processing collected signals and providing accurate diagnosis results. Although many studies have developed machine leaning (M.L) and deep learning (D.L) algorithms for detecting the bearing fault, the results have generally been limited to relatively small train and test datasets and the input data has been manipulated (selective features used) to reach high accuracy. In this work, the raw data, collected from accelerometers (time-domain features) are taken as the input of a novel temporal sequence prediction algorithm to present an end-to-end method for fault detection. We use equivalent temporal sequences as the input of a novel Convolutional Long-Short-Term-Memory Recurrent Neural Network (CRNN) to detect the bearing fault with the highest accuracy in the shortest possible time. The method can reach the highest accuracy in the literature, to the best knowledge of the authors of the present paper, voiding any sort of pre-processing or manipulation of the input data. Effectiveness and feasibility of the fault diagnosis method are validated by applying it to two commonly used benchmark real vibration datasets and comparing the result with the other intelligent fault diagnosis methods.

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References

  1. Bonnett AH, Yung C (2008) Increased efficiency versus increased reliability. IEEE Ind Appl Mag 14(1):29–36. https://doi.org/10.1109/MIA.2007.909802

    Article  Google Scholar 

  2. Mao W, Feng W, Liang X (2019) A novel deep output kernel learning method for bearing fault structural diagnosis. Mech Syst Signal Process 117:293–318. https://doi.org/10.1016/j.ymssp.2018.07.034

    Article  Google Scholar 

  3. Lessmeier C, Kimotho J, Zimmer D, Sextro W (2016). Condition monitoring of bearing damage in electromechanical drive systems by using motor current signals of electric motors: a benchmark data set for data-driven classification

  4. Bellini A, Immovilli F, Rubini R, Tassoni C (2008). Diagnosis of bearing faults of induction machines by vibration or current signals: A Critical Comparison. In: 2008 IEEE Industry Applications Society Annual Meeting, 5–9 Oct. 2008. pp 1–8. doi:https://doi.org/10.1109/08IAS.2008.26

  5. Zhou W, Habetler TG, Harley RG (2008) Bearing fault detection via Stator current noise cancellation and statistical control. IEEE Trans Ind Electron 55(12):4260–4269. https://doi.org/10.1109/TIE.2008.2005018

    Article  Google Scholar 

  6. Schoen RR, Habetler TG, Kamran F, Bartfield RG (1995) Motor bearing damage detection using stator current monitoring. IEEE Trans Ind Appl 31(6):1274–1279. https://doi.org/10.1109/28.475697

    Article  Google Scholar 

  7. Eren L, Devaney MJ (2004) Bearing damage detection via wavelet packet decomposition of the stator current. IEEE Trans Instrum Meas 53(2):431–436. https://doi.org/10.1109/TIM.2004.823323

    Article  Google Scholar 

  8. Samanta B, Nataraj C (2009) Use of particle swarm optimization for machinery fault detection. Eng Appl Artif Intell 22(2):308–316. https://doi.org/10.1016/j.engappai.2008.07.006

    Article  Google Scholar 

  9. MF Yaqub, Gondal I, Kamruzzaman J (2012). Inchoate Fault Detection Framework: Adaptive Selection of Wavelet Nodes and Cumulant Orders, vol 61. doi:https://doi.org/10.1109/TIM.2011.2172112, 61, 695

  10. Hu Q, He Z, Zhang Z, Zi Y (2007). Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMs ensemble, vol 21. doi:https://doi.org/10.1016/j.ymssp.2006.01.00721, 705

  11. Zhang R, Peng Z, Wu L, Yao B, Guan Y (2017) Fault diagnosis from raw sensor data using deep neural networks considering temporal coherence. Sensors 17:549. https://doi.org/10.3390/s17030549

    Article  Google Scholar 

  12. Eren L, Ince T, Kiranyaz S (2018) A generic intelligent bearing fault diagnosis system using compact adaptive 1D CNN classifier. Journal of Signal Processing Systems 91:179–189. https://doi.org/10.1007/s11265-018-1378-3

    Article  Google Scholar 

  13. Pan H, He X, Tang S, Meng F (2018). An improved bearing fault diagnosis Methodusing one-dimensional CNN and LSTM. doi:https://doi.org/10.5545/sv-jme.2017.5249

  14. Yoshimatsu O, Satou Y, Shibasaki K (2018) Rolling bearing diagnosis based on deep learning enhanced by various dataset Training多様データセットを用いた深層学習による転がり軸受の損傷診断. The Proceedings of the Symposium on Evaluation and Diagnosis 2018(17):109. https://doi.org/10.1299/jsmesed.2018.17.109

    Article  Google Scholar 

  15. J. Lee HQ, G. Yu, J. Lin, And Rexnord Technical Services (2017) IMS, University of Cincinnati. “bearing data set”, NASA Ames prognostics data repository. Center for Intelligent Maintenance Systems (IMS), University of Cincinnati

  16. Case Western Reserve University Bearing Data Center Website. (http://www.csegroupscaseedu/bearingdatacenter/home). Accessed 8 Sept 2018

  17. Alex K, Sutskever I, Hinton GE (2012). ImageNet classification with deep convolutional Neural Netw1097--1105

  18. Kiranyaz S, Avci O, Abdeljaber O, Ince T, Gabbouj M, Inman D (2019). 1D convolutional neural networks and applications: a survey

  19. Chen Y, Jiang H, Li C, Jia X, Ghamisi P (2016). Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks, vol 54. doi:https://doi.org/10.1109/TGRS.2016.258410754, 6251

  20. Zhen Zuo BS, Wang G, Liu X, Wang X, Wang B, Chen Y (2016) Learning contextual dependencies with convolutional hierarchical recurrent neural networks. IEEE Trans Image Process 25:2983–2996. https://doi.org/10.1109/TIP.2016.2548241

    Article  MathSciNet  MATH  Google Scholar 

  21. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735

    Article  Google Scholar 

  22. Zhang Y, Hao X, Liu Y (2019) Simplifying long short-term memory for fast training and time series prediction. J Phys Conf Ser 1213:042039. https://doi.org/10.1088/1742-6596/1213/4/042039

    Article  Google Scholar 

  23. Bilgera C, Yamamoto A, Sawano M, Matsukura H, Ishida H (2018) Application of convolutional long short-term memory neural networks to signals collected from a sensor network for autonomous gas source localization in outdoor environments. Sensors 18(12):4484

    Article  Google Scholar 

  24. Yao H, Wu F, Ke J, Tang X, Jia Y, Lu S, Gong P, Ye J (2018). Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction

  25. Huang C-J, Kuo P-H (2018). A deep CNN-LSTM model for particulate matter (PM2.5) forecasting in smart Cities, vol 18. doi:https://doi.org/10.3390/s18072220

  26. Shi X, Chen Z, Wang H, Yeung D-Y, Wong WK, Woo W-C (2015). Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting

  27. Lecun Y, Bengio Y (1995). Convolutional networks for images, Speech, and Time-Series

  28. Zhang X, Chen F, Huang R (2018) A combination of RNN and CNN for attention-based relation classification. Procedia Computer Science 131:911–917. https://doi.org/10.1016/j.procs.2018.04.221

    Article  Google Scholar 

  29. Sainath TN, Vinyals O, Senior A, Sak H (2015). Convolutional, Long Short-Term Memory, fully connected Deep Neural Networks. In: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 19–24 April 2015. pp 4580–4584. doi:https://doi.org/10.1109/ICASSP.2015.7178838

  30. Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: A Simple Way to Prevent Neural Networks from Overfitting, vol 15

  31. Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift

  32. Qiu H, Lee J, Lin J, Yu G (2006) Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics. J Sound Vib 289(4):1066–1090. https://doi.org/10.1016/j.jsv.2005.03.007

    Article  Google Scholar 

  33. Claessens BJ, Vrancx P, Ruelens F (2018) Convolutional neural networks for automatic state-time feature extraction in reinforcement learning applied to residential load control. IEEE Transactions on Smart Grid 9(4):3259–3269. https://doi.org/10.1109/TSG.2016.2629450

    Article  Google Scholar 

  34. Wang Y, Liu F, Zhu A (2019). Bearing fault diagnosis based on a hybrid classifier ensemble approach and the improved Dempster-Shafer theory. Sensors (Basel) 19 (9). doi:https://doi.org/10.3390/s19092097

  35. Zhang R, Peng Z, Wu L, Yao B, Guan Y (2017). Fault Diagnosis from Raw Sensor Data Using Deep Neural Networks Considering Temporal Coherence, vol 17. doi:https://doi.org/10.3390/s1703054917

  36. Zhou F, Yang S, Fujita H, Chen D, Wen C (2020) Deep learning fault diagnosis method based on global optimization GAN for unbalanced data. Knowl-Based Syst 187:104837. https://doi.org/10.1016/j.knosys.2019.07.008

    Article  Google Scholar 

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Acknowledgements

The authors are grateful to Dr.Maryam Amirmazlaghani for her helpful comments and constructive suggestions.

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Authors and Affiliations

  1. Department of Mechanical Engineering of WSE Co., Tehran, 1416844816, Iran

    Amin Khorram

  2. Department of IT and Computer Engineering, Amirkabir University of Technology, 15875-4413, Tehran, Iran

    Mohammad Khalooei

  3. Department of Computer Science, Tarbiat Modares University, Tehran, 14115-111, Iran

    Mansoor Rezghi

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  1. Amin Khorram
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  2. Mohammad Khalooei
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  3. Mansoor Rezghi
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Correspondence to Amin Khorram.

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Khorram, A., Khalooei, M. & Rezghi, M. End-to-end CNN + LSTM deep learning approach for bearing fault diagnosis. Appl Intell 51, 736–751 (2021). https://doi.org/10.1007/s10489-020-01859-1

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