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A sparse auto-encoder method based on compressed sensing and wavelet packet energy entropy for rolling bearing intelligent fault diagnosis

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Abstract

Improving diagnostic efficiency and shortening diagnostic time is important for improving the reliability and safety of rotating machinery, and has received more and more attention. When using intelligent diagnostic methods to diagnose bearing faults, the increasingly complex working conditions and the huge amount of data make it a great challenge to diagnose fault quickly and effectively. In this paper, a novel fault diagnosis method based on sparse auto-encoder (SAE), combined with compression sensing (CS) and wavelet packet energy entropy (WPEE) for feature dimension reduction is proposed. Firstly, vibration signals of each fault type are projected linearly through compressed sensing to obtain compressed signals, which are merged into a low-dimensional compressed signal matrix of multiple fault types. Secondly, the WPEE of low-dimensional compressed signal matrix of multi-fault type is determined, and the eigenvector matrix of bearing fault diagnosis is formed, which greatly reduces the dimension of the eigenvector matrix. Finally, SAE are constructed by adding sparse penalty to auto-encoder (AE) for high-level feature learning and bearing fault classification, and it not only further learns the high-level features of data, but also reduces the feature dimension. Compared with traditional feature extraction methods and the standard deep learning method, the proposed method not only guarantees high accuracy, but also greatly reduces the diagnosis time.

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References

  1. Y. G. Lei, F. Jia, J. Lin, S. B. Xing and S. X. Ding, An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data, IEEE Transactions on Industrial Electronics, 63 (5) (2016) 137–3147.

    Article  Google Scholar 

  2. Y. Wang, J. Xiang, Q. Mo and S. He, Compressed sparse time-frequency feature representation via compressive sensing and its applications in fault diagnosis, Measurement, 68 (2015) 70–81.

    Article  Google Scholar 

  3. H. Shao, H. Jiang, F. Wang and Y. Wang, Rolling bearing fault diagnosis using adaptive deep belief network with dual-tree complex wavelet packet, ISA Transactions, 69 (2017) 187–201.

    Article  Google Scholar 

  4. X. Yan, M. Jia, W. Zhang and L. Zhu, Fault diagnosis of rolling element bearing using a new optimal scale morphology analysis method, ISA Transactions, 73 (2018) 165–180.

    Article  Google Scholar 

  5. V. T. Thang, N. A. Tuan and N. V. Tiep, Evaluation of grinding wheel wear in wet profile grinding for the groove of the ball bearing’s inner ring by pneumatic probes, Journal of Mechanical Science and Technology, 32 (3) (2018) 1297–1305.

    Article  Google Scholar 

  6. N. Upadhyay and P. K. Kankar, Diagnosis of bearing defects using tunable Q-wavelet transform, Journal of Mechanical Science and Technology, 32 (2) (2018) 549–558.

    Article  Google Scholar 

  7. D. Y. Dou, J. Jiang, Y. Wang and Y. Zhang, A rule-based classifier ensemble for fault diagnosis of rotating machinery, Journal of Mechanical Science and Technology, 32 (6) (2018) 2509–2515.

    Article  Google Scholar 

  8. R. R. Fu, H. Wang, M. M. Han, D. Y. Han and J. D. Sun, Scaling analysis of phase fluctuations of brain networks in dynamic constrained object manipulation, International Journal of Neural Systems, 32 (1) (2018) 91–99.

    Google Scholar 

  9. B. Pang, G. Tang, C. Zhou and T. Tian, Rotor fault diagnosis based on characteristic frequency band energy entropy and support vector machine, Entropy, 20 (12) (2018) 932.

    Article  Google Scholar 

  10. G. C. Silva, R. M. Palhares and W. M. Caminhas, Immune inspired fault detection and diagnosis: A fuzzy-based approach of the negative selection algorithm and participatory clustering, Expert Systems with Applications, 39 (16) (2012) 12474–12486.

    Article  Google Scholar 

  11. N. Bayar, S. Darmoul, S. Hajri-Gabouj and H. Pierreval, Fault detection, diagnosis and recovery using artificial immune systems: A review, Engineering Applications of Artificial Intelligence, 46 (2015) 43–57.

    Article  Google Scholar 

  12. T. Benkedjouh, N. Zerhouni and S. Rechak, Tool wear condition monitoring based on continuous wavelet transform and blind source separation, International Journal of Advanced Manufacturing Technology, 97 (2018) 3311–3323.

    Article  Google Scholar 

  13. H. C. Sun, L. Fang and J. Z. Guo, A fault feature extraction method of rotating shaft with multiple weak faults based on underdetermined blind source signal, Measurement Science and Technology, 29 (2018).

  14. Y. Song, J. Liu, N. Chu, P. Wu and D. Wu, A novel demodulation method for rotating machinery based on time-frequency analysis and principal component analysis, Journal of Sound and Vibration, 442 (2019) 645–656.

    Article  Google Scholar 

  15. H. P. Zhang and Y. Deng, Engine fault diagnosis based on sensor data fusion considering information quality and evidence theory, Advances in Mechanical Engineering, 10 (11) (2018).

    Google Scholar 

  16. J. Q. Liu, A. F. Chen and N. Zhao, An intelligent fault diagnosis method for bogie bearings of metro vehicles based on weighted improved D-S evidence theory, Energies, 11 (2018) 232.

    Article  Google Scholar 

  17. F. Y. Xiao, Multi-sensor data fusion based on the belief divergence measure of evidences and the belief entropy, Information Fusion, 46 (2019) 23–32.

    Article  Google Scholar 

  18. J. He, M. Cao, W. Wang and X. Zhu, Partial discharge pattern recognition algorithm based on sparse self - coding and extreme learning machine, High Voltage Apparatus, 11 (2018).

  19. A. Widodo and B. S. Yang, Support vector machine in machine condition monitoring and fault diagnosis, Mech. Syst. Signal Process, 21 (6) (2007) 2560–2574.

    Article  Google Scholar 

  20. Y. G. Lei, Z. J. He and Y. Y. Zi, A new approach to intelligent fault diagnosis of rotating machinery, Expert Systems With Applications, 35 (4) (2008) 1593–1600.

    Article  Google Scholar 

  21. Z. B. Xu, J. B. Xuan, T. L. Shi, B. Wu and Y. M. Hu, A novel fault diagnosis method of bearing based on improved fuzzy ARTMAP and modified distance discriminant technique, Expert Systems With Applications, 36 (9) (2009) 11801–11807.

    Article  Google Scholar 

  22. C. He, C. Liu, Y. Li and J. Yuan, Intelligent fault diagnosis of rotating machinery based on multiple relevance vector machines with variance radial basis function kernel, Proceedings of The Institution of Mechanical Engineers Part C-Journal of Mechanical Engineering Science, 225 (2011) 1718–1729.

    Article  Google Scholar 

  23. H. Q. Wang and P. Chen, Intelligent diagnosis method for rolling element bearing faults using possibility theory and neural network, Computers & Industrial Engineering, 60 (4) (2011) 511–518.

    Article  Google Scholar 

  24. K. Li, P. Chen and H. Wang, Intelligent diagnosis method for rotating machinery using wavelet transform and ant colony optimization, IEEE Sensors Journal, 12 (2012) 2474–2484.

    Article  Google Scholar 

  25. K. Li, P. Chen and S. M. Wang, An intelligent diagnosis method for rotating machinery using least squares mapping and a fuzzy neural network, Sensors, 12 (5) (2012) 5919–5939.

    Article  Google Scholar 

  26. D. Y. Dou, J. G. Yang, J. T. Liu and Y. K. Zhao, A rule-based intelligent method for fault diagnosis of rotating machinery, Knowledge-Based Systems, 36 (2012) 1–8.

    Article  Google Scholar 

  27. G. Georgoulas, P. Karvelis, T. Loutas and C. D. Stylios, Rolling element bearings diagnostics using the symbolic aggregate approximation, Mech. Syst. Signal Process, 60-61 (2015) 229–242.

    Article  Google Scholar 

  28. X. L. Zhang, W. Chen, B. J. Wang and X. F. Chen, Intelligent fault diagnosis of rotating machinery using support vector machine with ant colony algorithm for synchronous feature selection and parameter optimization, Neurocomputing, 167 (2015) 260–279.

    Article  Google Scholar 

  29. F. Jia, Y. G. Lei, J. Lin, X. Zhou and N. Lu, Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data, Mech. Syst. Signal Process, 72-73 (2016) 303–315.

    Article  Google Scholar 

  30. H. D. Shao, H. K. Jiang, H. Z. Zhang, W. J. Duan, T. C. Liang and S. P. Wu, Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing, Mech. Syst. Signal Process, 100 (2018) 743–765.

    Article  Google Scholar 

  31. H. Shao, H. Jiang, H. Zhang and T. Liang, Electric locomotive bearing fault diagnosis using a novel convolutional deep belief network, IEEE Transactions on Industrial Electronics, 99 (2017) 2727–2736.

    Google Scholar 

  32. I. Attoui, N. Fergani, N. Boutasseta, B. Oudjani and A. Deliou, A new time-frequency method for identification and classification of ball bearing faults, Journal of Sound and Vibration, 397 (2017) 241–265.

    Article  Google Scholar 

  33. Z. H. Du, X. F. Chen, H. Zhang and B. Y. Yang, Compressedsensing-based periodic impulsive feature detection for wind turbine systems, IEEE Transactions on Industrial Informatics (2017) 2933–2945.

    Google Scholar 

  34. Z. Du, X. Chen, H. Zhang, H. Miao, Y. Guo and B. Yang, Feature identification with compressive measurements for machine fault diagnosis, IEEE Transactions on Instrumentation and Measurement, 65 (2016) 977–987.

    Article  Google Scholar 

  35. Y. Wang, J. Xiang, Q. Mo and S. He, Compressed sparse time-frequency feature representation via compressive sensing and its applications in fault diagnosis, Measurement, 68 (2015) 70–81.

    Article  Google Scholar 

  36. J. Sun, C. Yan and J. Wen, Intelligent bearing fault diagnosis method combining compressed data acquisition and deep learning, IEEE Transactions on Instrumentation and Measurement, 99 (2017) 185–195.

    Google Scholar 

  37. P. Ma, H. Zhang, W. Fan, C. Wang and X. Zhang, A novel bearing fault diagnosis method based on 2D image representation and transfer learning-convolutional neural network, Measurement Science and Technology, 30 (2019) 055402.

    Article  Google Scholar 

  38. H. Shao, H. Jiang and W. Duan, Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing, Mechanical Systems and Signal Processing, 100 (2018) 743–765.

    Article  Google Scholar 

  39. D. L. Donoho, Compressed sensing, IEEE Transactions on Information Theory, 52 (4) (2006) 1289–1306.

    Article  MathSciNet  MATH  Google Scholar 

  40. E. J. Candes and T. Tao, Near-optimal signal recovery from random projections: Universal encoding strategies, IEEE Transactions on Information Theory, 52 (12) (2006) 5406–5425.

    Article  MathSciNet  MATH  Google Scholar 

  41. G. Tang, Q. Yang, H. Q. Wang, G. G. Luo and J. W. Ma, Sparse classification of rotating machinery faults based on compressive sensing strategy, Mechatronics, 31 (2015) 22–29.

    Article  Google Scholar 

  42. D. Takhar et al., A compressed sensing camera: New theory and an implementation using digital micromirrors, Proc. of Computational Imaging IV (2006).

    Google Scholar 

  43. K. Zhu, X. Lin, K. Li and L. Jiang, Compressive sensing and sparse decomposition in precision machining process monitoring: From theory to applications, Mechatronics, 31 (2015) 3–15.

    Article  Google Scholar 

  44. C. Liu, X. Wu, J. Mao and X. Liu, Acoustic emission signal processing for rolling bearing running state assessment using compressive sensing, Mech. Syst. Signal Process, 91 (2017) 395–406.

    Article  Google Scholar 

  45. X. Wang, Z. Zhao, Y. Xia and H. Zhang, Compressed sensing for efficient random routing in multi-hop wireless sensor networks, International Journal of Communication Networks and Distributed Systems, 7 (3/4) (2011) 275–292.

    Article  Google Scholar 

  46. T. Han, C. Liu, L. J. Wu, S. Sarkar and D. X. Jiang, An adaptive spatiotemporal feature learning approach for fault diagnosis in complex systems, Mech. Syst. Signal Process, 117 (2019) 170–187.

    Article  Google Scholar 

  47. C. Wang, M. Gan and C. Zhu, A supervised sparsity-based wavelet feature for bearing fault diagnosis, Journal of Intelligent Manufacturing, 30 (2019) 229–239.

    Article  Google Scholar 

  48. Z. Y. He, Y. M. Cai and Q. Q. Qian, Study of wavelet entropy theory and its application in electric power system fault detection, Proceedings of the Csee, 5 (2005).

  49. M. Y. Yang and Y. K. Yang, A study of transient-based protection using wavelet energy entropy for power system ehv transmission line, 2010 International Conference on Wavelet Analysis and Pattern Recognition (2010) 283–288.

    Chapter  Google Scholar 

  50. S. Li and Z. Liu, Application of improved wavelet packet energy entropy and GA-SVM in rolling bearing fault diagnosis, 2018 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) (2018).

    Google Scholar 

  51. H. Ghodrati and A. Hamza, Nonrigid 3D shape retrieval using deep auto-encoders, Proceedings of the 2017 International Conference on Communications, Signal Processing, and Systems, 47 (2017) 44–61.

    Google Scholar 

  52. K. Han, Designing extreme learning machine network structure based on tolerance rough set, International Journal of Intelligent Information Technologies, 13 (2017) 38–55.

    Article  Google Scholar 

  53. Z. Zhang, Y. Wang and K. Wang, Fault diagnosis and prognosis using wavelet packet decomposition, Fourier transform and artificial neural network, Journal of Intelligent Manufacturing, 24 (6) (2013) 1213–1227.

    Article  Google Scholar 

  54. J. Liu, A. Chen and N. Zhao, An intelligent fault diagnosis method for bogie bearings of metro vehicles based on weighted improved D-S evidence theory, Energies, 11 (2018) 232.

    Article  Google Scholar 

  55. C. Hou, Y. Li and Y. Cao, Analysis on vibration and acoustic joint mechanical fault diagnosis of high voltage vacuum circuit based on wavelet packet energy relative entropy, 2016 27th International Symposium on Discharges and Electrical Insulation in Vacuum (ISDEIV) (2016).

    Google Scholar 

  56. L. Wen, L. Gao and X. Li, A new deep transfer learning based on sparse auto encoder for fault diagnosis, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 49 (2019) 136–144.

    Article  Google Scholar 

  57. H. Liu, J. Zhou, Y. Zheng, W. Jiang and Y. Zhang, Fault diagnosis of rolling bearings with recurrent neural network-based auto encoders, ISA Transactions, 77 (2018) 167–178.

    Article  Google Scholar 

  58. C. Wang, M. Gan and C. A. Zhu, Fault feature extraction of rolling element bearings based on wavelet packet transform and sparse representation theory, Journal of Intelligent Manufacturing, 29 (2018) 937–951.

    Article  Google Scholar 

  59. Z. Zhang, Y. Wang and K. Wang, Fault diagnosis and prognosis using wavelet packet decomposition, Fourier transform and artificial neural network, Journal of Intelligent Manufacturing, 24 (6) (2013) 1213–1227.

    Article  Google Scholar 

  60. C. Wang, M. Gan and C. Zhu, Intelligent fault diagnosis of rolling element bearings using sparse wavelet energy based on overcomplete DWT and basis pursuit, Journal of Intelligent Manufacturing, 28 (6) (2017) 1377–1391.

    Article  Google Scholar 

  61. Y. Qiu, W. Zhou, N. Yu and P. Du, Denoising sparse autoencoder based ictal EEG classification, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 99 (2018).

  62. G. Helbing and M. Ritter, Deep learning for fault detection in wind turbines, Renewable and Sustainable Energy Reviews, 98 (2018) 189–198.

    Article  Google Scholar 

  63. H. O. A. Ahmed, M. L. D. Wong and A. K. Nandi, Intelligent condition monitoring method for bearing faults from highly compressed measurements using sparse over-complete features, Mech. Syst. Signal Process, 99 (2018) 459–477.

    Article  Google Scholar 

  64. J. Sun, C. Yan and J. Wen, Intelligent bearing fault diagnosis method combining compressed data acquisition and deep learning, IEEE Transactions on Instrumentation and Measurement, 67 (2018) 185–195.

    Article  Google Scholar 

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Acknowledgments

The studies were funded by the National Natural Science Foundation of China [Grant numbers 61973262 and 51875500], Natural Science Foundation of Hebei Province (Grant number E2019203146), and Hebei Province Graduate Innovation Funding Project [Grant number 2019000629].

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Authors and Affiliations

  1. School of Electrical Engineering, Yanshan University, Qinhuangdao, 066004, China

    Peiming Shi, Xiaoci Guo & Rongrong Fu

  2. School of Vehicles and Energy, Yanshan University, Qinhuangdao, 066004, China

    Dongying Han

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  1. Peiming Shi
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  2. Xiaoci Guo
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  3. Dongying Han
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  4. Rongrong Fu
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Correspondence to Peiming Shi.

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Recommended by Editor No-cheol Park

Peiming Shi received Ph.D. degree in information science and engineering institute from Yanshan University, Qinhuangdao, China, in 2009. Now he is a Professor in Institute of Electrical Engineering of Yanshan University. His current research interests include fault diagnosis and signal processing.

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Shi, P., Guo, X., Han, D. et al. A sparse auto-encoder method based on compressed sensing and wavelet packet energy entropy for rolling bearing intelligent fault diagnosis. J Mech Sci Technol 34, 1445–1458 (2020). https://doi.org/10.1007/s12206-020-0306-1

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  • DOI: https://doi.org/10.1007/s12206-020-0306-1

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