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http://dx.doi.org/10.3745/KTSDE.2022.11.4.179

An Input Transformation with MFCCs and CNN Learning Based Robust Bearing Fault Diagnosis Method for Various Working Conditions  

Seo, Yangjin ((주)이포즌)
Publication Information
KIPS Transactions on Software and Data Engineering / v.11, no.4, 2022 , pp. 179-188 More about this Journal
Abstract
There have been many successful researches on a bearing fault diagnosis based on Deep Learning, but there is still a critical issue of the data distribution difference between training data and test data from their different working conditions causing performance degradation in applying those methods to the machines in the field. As a solution, a data adaptation method has been proposed and showed a good result, but each and every approach is strictly limited to a specific applying scenario or presupposition, which makes it still difficult to be used as a real-world application. Therefore, in this study, we have proposed a method that, using a data transformation with MFCCs and a simple CNN architecture, can perform a robust diagnosis on a target domain data without an additional learning or tuning on the model generated from a source domain data and conducted an experiment and analysis on the proposed method with the CWRU bearing dataset, which is one of the representative datasests for bearing fault diagnosis. The experimental results showed that our method achieved an equal performance to those of transfer learning based methods and a better performance by at least 15% compared to that of an input transformation based baseline method.
Keywords
Bearing Fault Diagnosis; Deep Learning; Distribution Difference; MFCCs; CNN;
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Times Cited By KSCI : 3  (Citation Analysis)
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1 R. Yan, R. X. Gao, and X. Chen, "Wavelets for fault diagnosis of rotary machines: A review with applications," Signal Processing, Vol.96, pp.1-15, 2014.   DOI
2 Y. Lei, F. Jia, J. Lin, S. Xing, and S. X. Ding, "An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data," IEEE Transactions on Industrial Electronics, Vol.63, No.5, pp.3137-3147, 2016.   DOI
3 W. Zhang, G. Peng, C. Li, Y. Chen, and Z. Zhang, "A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals," Sensors, Vol.17, No.2, p.425-445, 2017.   DOI
4 G. Jin, T. Zhu, M. W. Akram, Y. Jin, and C. Zhu, "An adaptive anti-noise neural network for bearing fault diagnosis under noise and varying load conditions," IEEE Access, Vol.8, pp.74793-74807, 2020.   DOI
5 P. Wang and G. Vachtsevanos, "Fault prognostics using dynamic wavelet neural networks," AI EDAM, Vol.15, No.4, pp.349-365, 2001.
6 D. An, N. H. Kim, and J. H. Choi, "Practical options for selecting data-driven or physics-based prognostics algorithms with reviews," Reliability Engineering & System Safety, Vol.133, pp.223-236, 2015.   DOI
7 A. Cubillo, S. Perinpanayagam, and M. Esperon-Miguez, "A review of physics-based models in prognostics: Application to gears and bearings of rotating machinery," Advances in Mechanical Engineering, Vol.8, No.8, pp.1-21, 2016.
8 X. Wang and F. Liu, "Triplet loss guided adversarial domain adaptation for bearing fault diagnosis," Sensors, Vol.20, No.1, pp.320-338, 2020.   DOI
9 D. Wang, K. L. Tsui, and Q. Miao, "Prognostics and health management: A review of vibration based bearing and gear health indicators," IEEE Access, Vol.6, pp.665-676, 2017.   DOI
10 K. L. Tsui, N. Chen, Q. Zhou, Y. Hai, and W. Wang, "Prognostics and health management: A review on data driven approaches," Mathematical Problems in Engineering, 2015.
11 L. Jing, M. Zhao, P. Li, and X. Xu, "A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox," Measurement, Vol.111, pp.1-10, 2017.   DOI
12 I. Goodfellow, Y. Bengio, and A. Courville, "Deep learning," Cambridge: MIT press, 2016.
13 A. Zhang, S. Li, Y. Cui, W. Yang, R. Dong, and J. Hu, "Limited data rolling bearing fault diagnosis with few-shot learning," IEEE Access, Vol.7, pp.110895-110904, 2019.   DOI
14 L. I. Xueyi, L. I. Jialin, Q. U. Yongzhi, and H. E. David, "Semi-supervised gear fault diagnosis using raw vibration signal based on deep learning," Chinese Journal of Aeronautics, Vol.33, No.2, pp.418-426, 2020.   DOI
15 H. Li, Q. Zhang, X. Qin, and S. Yuantao, "Raw vibration signal pattern recognition with automatic hyper-parameter-optimized convolutional neural network for bearing fault diagnosis," Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, Vol.234, No.1, pp.343-360, 2020.   DOI
16 X. Li, W. Zhang, Q. Ding, and J. Q. Sun, "Multi-layer domain adaptation method for rolling bearing fault diagnosis," Signal Processing, Vol.157, pp.180-197, 2019.   DOI
17 W. Zhang, G. Peng, and C. Li, "Bearings fault diagnosis based on convolutional neural networks with 2-D representation of vibration signals as input," in Proceedings of the 3rd International Conference on Mechatronics and Mechanical Engineering, Shanghai, pp.13001-13005, 2016.
18 U. E. Akpudo and J. W. Hur, "A cost-efficient MFCC-based fault detection and isolation technology for electromagnetic pumps," Electronics, Vol.10, No.4, pp.439-458, 2021.   DOI
19 M. J. Hasan and J. M. Kim, "Bearing fault diagnosis under variable rotational speeds using stockwell transform-based vibration imaging and transfer learning," Applied Sciences, Vol.8, No.12, p.2357-2071, 2018.   DOI
20 S. Lee, et al., "A study on deep learning application of vibration data and visualization of defects for predictive maintenance of gravity acceleration equipment," Applied Sciences, Vol.11, No.4, pp.1564-1578, 2021.   DOI
21 A. Lerch, "An introduction to audio content analysis: Applications in signal processing and music informatics," Wiley-IEEE Press, 2012.
22 Librosa [Internet], https://github.com/librosa/librosa
23 S. Gupta, J. Jaafar, W. W. Ahmad, and A. Bansal, "Feature extraction using MFCC," Signal & Image Processing: An International Journal, Vol.4, No.4, pp.101-108, 2013.   DOI
24 K. O'Shea and R. Nash, "An introduction to convolutional neural networks," arXiv preprint arXiv:1511.08458, 2015.
25 S. Tang, S. Yuan, and Y. Zhu, "Data preprocessing techniques in convolutional neural network based on fault diagnosis towards rotating machinery," IEEE Access, Vol.8, pp.149487-149496, 2020.   DOI
26 S. Albawi, T. A. Mohammed, and S. Al-Zawi, "Understanding of a convolutional neural network," In International Conference on Engineering and Technology, Antalya, 2017, pp.1-6.
27 Case Western Reserve University (CWRU) Bearing Data Center [Internet], https://csegroups.case.edu/bearingdatacenter/home.
28 D. Neupane and J. Seok, "Bearing fault detection and diagnosis using case western reserve university dataset with deep learning approaches: A review," IEEE Access, Vol.8, pp.93155-93178, 2020.   DOI
29 L. Alzubaidi, et al., "Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions," Journal of Big Data, Vol.8, No.1, pp.1-74, 2021.   DOI
30 S. A. Alim and N. K. A. Rashid, "Some commonly used speech feature extraction algorithms," in From Natural to Artificial Intelligence, IntechOpen., ch.1, pp.2-119, 2018.
31 R. Zhang, H. Tao, L. Wu, and Y. Guan, "Transfer learning with neural networks for bearing fault diagnosis in changing working conditions," IEEE Access, Vol.5, pp.14347-14357, 2017.   DOI
32 X. Ding, X. Zhang, N. Ma, J. Han, G. Ding, and J. Sun, "Repvgg: Making vgg-style convnets great again," In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.13733-13742, 2021.
33 B. Zhang, W. Li, X. L. Li, and S. K. Ng, "Intelligent fault diagnosis under varying working conditions based on domain adaptive convolutional neural networks," IEEE Access, Vol.6, pp.66367-66384, 2018.   DOI
34 J. He, X. Li, Y. Chen, D. Chen, J. Guo, and Y. Zhou, "Deep transfer learning method based on 1D-CNN for bearing fault diagnosis," Shock and Vibration, 2021.
35 Y. Du, A. Wang, S. Wang, B. He, and G. Meng, "Fault diagnosis under variable working conditions based on STFT and transfer deep residual network," Shock and Vibration, 2020.
36 Z. G. Cheng, W. J. Liao, X. Y. Chen, and X. Z. Lu, "A vibration recognition method based on deep learning and signal processing," Engineering Mechanics, Vol.38, No.4, pp.230-246, 2021.
37 F. Zheng, G. Zhang, and Z. Song, "Comparison of different implementations of MFCC," Journal of Computer Science and Technology, Vol.16, No.6, pp.582-589, 2001.   DOI
38 S. Zhang, S. Zhang, B. Wang, and T. G. Habetler, "Deep learning algorithms for bearing fault diagnostics: A comprehensive review," IEEE Access, Vol.8, pp.29857-29881, 2020.   DOI
39 J. Jiao, M. Zhao, J. Lin, and K. Liang, "A comprehensive review on convolutional neural network in machine fault diagnosis," Neurocomputing, Vol.417, pp.36-63, 2020.   DOI
40 J. Lee, F. Wu, W. Zhao, M. Ghaffari, L. Liao, and D. Siegel, "Prognostics and health management design for rotary machinery systems-Reviews, methodology and applications," Mechanical Systems and Signal Processing, Vol.42, No.1-2, pp.314-334, 2014.   DOI
41 H. Qiu, J. Lee, J. Lin, and G. Yu, "Robust performance degradation assessment methods for enhanced rolling element bearing prognostics," Advanced Engineering Informatics, Vol.17, No.3-4, pp.127-140, 2003.   DOI
42 S. Nandi, H. A.Toliyat, and X. Li, "Condition monitoring and fault diagnosis of electrical motors-A review," IEEE Transactions on Energy Conversion, Vol.20, No.4, pp.719-729, 2005.   DOI
43 F. Camci, K. Medjaher, N. Zerhouni, and P. Nectoux, "Feature evaluation for effective bearing prognostics," Quality and Reliability Engineering International, Vol.29, No.4, pp.477-486, 2013.   DOI
44 M. Lebold, K. McClintic, R. Campbell, C. Byington, and K. Maynard, "Review of vibration analysis methods for gearbox diagnostics and prognostics," in Proceedings of the 54th Meeting of the Society for Machinery Failure Prevention Technology, Virginia Beach, pp.623-634, 2000.