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Intelligent fault diagnosis of rolling bearings based on LSTM with large margin nearest neighbor algorithm

  • S.I.: Deep learning modelling in real life: (Anomaly Detection, Biomedical, Concept Analysis, Finance, Image analysis, Recommendation)
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Abstract

In industrial machinery, rolling bearings are often rated as the most likely to fail in mechanical systems due to excessive working stress. Therefore, effective methods to diagnose the faults in rolling bearings are becoming necessary and required to ensure economic efficiency and manufacturing reliability. Recently, several studies tried to develop deep learning models for intelligent fault diagnosis based on traditional methods. However, creating an effective method for fault recognition is still a major obstacle due to varying operating conditions, large amount of collected data and redundant noise in measured vibration signals. Advanced signal processing techniques and traditional strategies often consist of shallow constructs that suffer from learning a huge amount of data, losing valuable fault information, yielding in low accuracy and labor time losses. In this paper, a combination method of long short-term memory with large margin nearest neighbor (LSTM-LMNN) is designed to address the above issues and effectively recognize multi-faults in mechanical rotating machines. Different from traditional LSTMs, the proposed LSTM-LMNN utilizes a powerful orthogonal weight initialization technique to memorize the critical information of faults during parameters updating and strongly organizing the samples of each condition in pattern classification process. Two experimental studies of bearing fault diagnosis demonstrate that the proposed LSTM-LMNN model outperformed other existing methods in terms of diagnostic efficiency, stability, and reliability.

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References

  1. Benbouzid MEH, Vieira M, Theys C (1999) Induction motors’ faults detection and localization using stator current advanced signal processing techniques. IEEE Trans Power Electron 14(1):14–22. https://doi.org/10.1109/63.737588

    Article  Google Scholar 

  2. Zhang P, Du Y, Habetler TG, Lu B (2009) A survey of condition monitoring and protection methods for medium voltage induction motors. In: 2009 IEEE energy conversion congress and exposition, pp 3165–3174. https://doi.org/10.1109/ECCE.2009.5316083

  3. O’Donnell P (1985) Report of large motor reliability survey of industrial and commercial installations, part I. IEEE Trans Ind Appl IA-21(4):853–864

    Article  Google Scholar 

  4. O’Donnell P (1987) Report of large motor reliability survey of industrial and commercial installations, part I. IEEE Trans Ind Appl IA-21(4):865–872

    Google Scholar 

  5. Lei Y, He Z, Zi Y (2009) Application of an intelligent classification method to mechanical fault diagnosis. Expert Syst Appl 36:9941–9948. https://doi.org/10.1016/j.eswa.2009.01.065

    Article  Google Scholar 

  6. Yonggang X, Tian W, Zhang K, Ma C (2018) Application of enhanced fast kurtogram based on empirical wavelet transform for bearing fault diagnosis. Measur Sci Technol 30:035001. https://doi.org/10.1088/1361-6501/aafb44

    Article  Google Scholar 

  7. Agrawal P, Jayaswal P (2020) Diagnosis and classifications of bearing faults using artificial neural network and support vector machine. J Inst Eng Ser C 101(1):61–72. https://doi.org/10.1007/s40032-019-00519-9

    Article  Google Scholar 

  8. Lin C-J, Chu W-L, Wang C-C, Chen C-K, Chen I-T (2019) Diagnosis of ball-bearing faults using support vector machine based on the artificial fish-swarm algorithm. J Low Freq Noise Vib Act Control 39(4):954–967. https://doi.org/10.1177/1461348419861822

    Article  Google Scholar 

  9. Fan Y, Zhang C, Xue Y, Wang J, Gu F (2020) A bearing fault diagnosis using a support vector machine optimised by the self-regulating particle swarm. Shock Vib 2020:9096852. https://doi.org/10.1155/2020/9096852

    Article  Google Scholar 

  10. Jia F, Lei Y, Lin J, Zhou X, Lu N (2015) Deep neural networks: a promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. Mech Syst Signal Process. https://doi.org/10.1016/j.ymssp.2015.10.025

    Article  Google Scholar 

  11. Shao H, Jiang H, Zhang H, Duan W, Liang T, Wu S (2018) Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing. Mech Syst Signal Process 100:743–765. https://doi.org/10.1016/j.ymssp.2017.08.002

    Article  Google Scholar 

  12. Shao H, Hongkai J, Huiwei Z, Fuan W (2016) An enhancement deep feature fusion method for rotating machinery fault diagnosis. Knowl-Based Syst 119:200–220. https://doi.org/10.1016/j.knosys.2016.12.012

    Article  Google Scholar 

  13. Lei Y, Jia F, Lin J, Xing S, Ding SX (2016) An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data. IEEE Trans Ind Electron 63(5):3137–3147. https://doi.org/10.1109/TIE.2016.2519325

    Article  Google Scholar 

  14. Leng J, Jiang P (2016) A deep learning approach for relationship extraction from interaction context in social manufacturing paradigm. Knowledge-Based Syst 100:188–199. https://doi.org/10.1016/j.knosys.2016.03.008

    Article  Google Scholar 

  15. Kaya Y, Kuncan M, Kaplan K, Minaz M, Ertunc HM (2020) Classification of bearing vibration speeds under 1D-LBP based on eight local directional filters. Soft Comput 24:12175–12186. https://doi.org/10.1007/s00500-019-04656-2

    Article  Google Scholar 

  16. Sun J, Xiao Z, Xie Y (2017) Automatic multi-fault recognition in TFDS based on convolutional neural network. Neurocomputing 222:127–136. https://doi.org/10.1016/j.neucom.2016.10.018

    Article  Google Scholar 

  17. Aljemely AH, Xuan J, Xu L, Jawad FKJ, Al-Azzawi O (2021) Wise-local response convolutional neural network based on Naïve Bayes theorem for rotating machinery fault classification. Appl Intell. https://doi.org/10.1007/s10489-021-02252-2

    Article  Google Scholar 

  18. Gu K, Zhang Y, Liu X, Li H, Ren M (2021) DWT-LSTM-based fault diagnosis of rolling bearings with multi-sensors. Electronics 10:2076. https://doi.org/10.3390/electronics10172076

    Article  Google Scholar 

  19. Mallak A, Fathi M (2021) Sensor and component fault detection and diagnosis for hydraulic machinery integrating LSTM autoencoder detector and diagnostic classifiers. Sensors 21(2):433. https://doi.org/10.3390/s21020433

    Article  Google Scholar 

  20. Chen Z, Li W, Gryllias K (2018) Gearbox fault diagnosis based on Convolutional Neural Networks

  21. Aljemely AH, Xuan J, Jawad FKJ, Al-Azzawi O, Alhumaima AS (2020) A novel unsupervised learning method for intelligent fault diagnosis of rolling element bearings based on deep functional auto-encoder. J Mech Sci Technol 34(11):4367–4381. https://doi.org/10.1007/s12206-020-1002-x

    Article  Google Scholar 

  22. Sharma A, Jigyasu R, Mathew L, Chatterji S (2018) Bearing fault diagnosis using weighted K-nearest neighbour. In: 2018 2nd international conference on trends in electronics and informatics (ICOEI), pp 1132–1137. https://doi.org/10.1109/ICOEI.2018.8553800

  23. Pandya DH, Upadhyay SH, Harsha SP (2013) Fault diagnosis of rolling element bearing with intrinsic mode function of acoustic emission data using APF-KNN. Expert Syst Appl 40(10):4137–4145. https://doi.org/10.1016/j.eswa.2013.01.033

    Article  Google Scholar 

  24. Baraldi P, Cannarile F, Di Maio F, Zio E (2016) Hierarchical k-nearest neighbours classification and binary differential evolution for fault diagnostics of automotive bearings operating under variable conditions. Eng Appl Artif Intell 56:1–13. https://doi.org/10.1016/j.engappai.2016.08.011

    Article  Google Scholar 

  25. Lu Q, Shen X, Wang X, Li M, Li J, Zhang M (2021) Fault diagnosis of rolling bearing based on improved VMD and KNN. Math Probl Eng 2021:2530315. https://doi.org/10.1155/2021/2530315

    Article  Google Scholar 

  26. Schmidhuber J (2014) Deep learning in neural networks: an overview. Neural Netw 61:85–117. https://doi.org/10.1016/j.neunet.2014.09.003

    Article  Google Scholar 

  27. 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 

  28. Weinberger K, Blitzer J, Saul L (2006) Distance Metric Learning for Large Margin Nearest Neighbor Classification. J Mach Learn Res 10:207–244

    MATH  Google Scholar 

  29. Xiang J, Zhong Y (2016) A novel personalized diagnosis methodology using numerical simulation and an intelligent method to detect faults in a shaft. Appl Sci 6(12):414. https://doi.org/10.3390/app6120414

    Article  Google Scholar 

  30. Bearing DataCenter, Paderborn University. https://mb.unipaderborn.de/kat/forschung/datacenter/bearing-datacenter/. Accessed December 2018

  31. van der Maaten L, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 9:2579–2605

    MATH  Google Scholar 

  32. Kang M, Islam DMDR, Kim J, Kim J, Pecht M (2016) A hybrid feature selection scheme for reducing diagnostic performance deterioration caused by outliers in data-driven diagnostics. IEEE Trans Ind Electron 63:1. https://doi.org/10.1109/TIE.2016.2527623

    Article  Google Scholar 

  33. Shao Y, Yuan X, Zhang C, Song Y, Xu Q (2020) A novel fault diagnosis algorithm for rolling bearings based on one-dimensional convolutional neural network and INPSO-SVM. Appl Sci 10(12):4303. https://doi.org/10.3390/app10124303

    Article  Google Scholar 

  34. Zhang W, Peng G, Li C, Chen Y, Zhang Z (2017) A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals. Sensors 17:425. https://doi.org/10.3390/s17020425

    Article  Google Scholar 

  35. MFPT Dataset. Society for machinery failure prevention technology. https://mfpt.org/fault-datasets/. Accessed 28 Mar 2020

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Acknowledgements

This research is supported by the National Key R&D Program of China (Grant No.2020YFB2007700) and the National Natural Science Foundation of China (Grant No. 52175094).

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Authors and Affiliations

  1. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, People’s Republic of China

    Anas H. Aljemely & Jianping Xuan

  2. School of Civil Engineering and Mechanics, Huazhong University of Science and Technology, Wuhan, 430074, People’s Republic of China

    Osama Al-Azzawi & Farqad K. J. Jawad

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  1. Anas H. Aljemely
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  2. Jianping Xuan
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  3. Osama Al-Azzawi
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  4. Farqad K. J. Jawad
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Correspondence to Jianping Xuan.

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Aljemely, A.H., Xuan, J., Al-Azzawi, O. et al. Intelligent fault diagnosis of rolling bearings based on LSTM with large margin nearest neighbor algorithm. Neural Comput & Applic 34, 19401–19421 (2022). https://doi.org/10.1007/s00521-022-07353-8

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  • DOI: https://doi.org/10.1007/s00521-022-07353-8

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