The application of audio signals in gear fault diagnosis based on deep learning methods: an end-to-end ap-proach
پذیرفته شده برای ارائه شفاهی ، صفحه 1-12 (12)
کد مقاله : 1098-ISAV2022 (R3)
نویسندگان
1دانشکده مهندسی مکانیک، دانشگاه صنعتی امیرکبیر
2Acoustics Research Lab., Mechanical Engineering Department, Amirkabir University of Technology
چکیده
Diagnosing gearbox faults based on audio signal has received less attention in researches, alt-hough due to the non-contact nature of the microphone, it makes the diagnosis process more accessible. In this article, based on the methods of deep learning, the diagnosis of crack and uniform wear of the gearbox in three different scenarios of (1) constant fault severity and working conditions, (2) constant fault severity and different working conditions, (3) different fault severity and different working conditions have been investigated. State-of-the-art methods of Convolutional Neural Network (CNN), Deep Residual Neural Network (DRN) and a proposed hybrid network of CNN and Long Short-Term Memory (LSTM), all applied based on end-to-end approach, have been investigated. The results show that the CNN+LSTM has a better performance than other methods, in such a way that in the most difficult case, i.e. different fault severity and different working conditions, it classifies the faults with an accuracy of 88.8%. In addition, the computational cost of training the pro-posed network is less than other networks.
کلیدواژه ها
موضوعات
Title
The application of audio signals in gear fault diagnosis based on deep learning methods: an end-to-end ap-proach
Authors
Hassan Alavi, Abdolreza Ohadi
Abstract
Diagnosing gearbox faults based on audio signal has received less attention in researches, alt-hough due to the non-contact nature of the microphone, it makes the diagnosis process more accessible. In this article, based on the methods of deep learning, the diagnosis of crack and uniform wear of the gearbox in three different scenarios of (1) constant fault severity and working conditions, (2) constant fault severity and different working conditions, (3) different fault severity and different working conditions have been investigated. State-of-the-art methods of Convolutional Neural Network (CNN), Deep Residual Neural Network (DRN) and a proposed hybrid network of CNN and Long Short-Term Memory (LSTM), all applied based on end-to-end approach, have been investigated. The results show that the CNN+LSTM has a better performance than other methods, in such a way that in the most difficult case, i.e. different fault severity and different working conditions, it classifies the faults with an accuracy of 88.8%. In addition, the computational cost of training the pro-posed network is less than other networks.
Keywords
Fault diagnosis, gearbox, Deep learning, End-to-end approach
مراجع
<p dir="ltr">[1] N. Baydar and A. Ball, "A comparative study of acoustic and vibration signals in detection of gear failures using Wigner–Ville distribution," Mechanical systems and signal processing, vol. 15, no. 6, pp. 1091-1107, 2001.</p>
<p dir="ltr">[2] J. Hou, W. Jiang, and W. Lu, "Application of a near-field acoustic holography-based diagnosis technique in gearbox fault diagnosis," Journal of Vibration Control, vol. 19, no. 1, pp. 3-13, 2013.</p>
<p dir="ltr">[3] Vanraj, S. Dhami, and B. Pabla, "Hybrid data fusion approach for fault diagnosis of fixed-axis gearbox," Structural Health Monitoring, vol. 17, no. 4, pp. 936-945, 2018.</p>
<p dir="ltr">[4] G. E. Hinton, S. Osindero, and Y.-W. Teh, "A fast learning algorithm for deep belief nets," Neural computation, vol. 18, no. 7, pp. 1527-1554, 2006.</p>
<p dir="ltr">[5] 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.</p>
<p dir="ltr">[6] L. Jing, T. Wang, M. Zhao, and P. Wang, "An adaptive multi-sensor data fusion method based on deep convolutional neural networks for fault diagnosis of planetary gearbox," Sensors, vol. 17, no. 2, p. 414, 2017.</p>
<p dir="ltr">[7] G. Jiang, H. He, J. Yan, and P. Xie, "Multiscale convolutional neural networks for fault diagnosis of wind turbine gearbox," IEEE Transactions on Industrial Electronics, vol. 66, no. 4, pp. 3196-3207, 2018.</p>
<p dir="ltr">[8] S. Kim and J.-H. Choi, "Convolutional neural network for gear fault diagnosis based on signal segmentation approach," Structural Health Monitoring, vol. 18, no. 5-6, pp. 1401-1415, 2018</p>
<p dir="ltr">[9] Z. Minghang, M. Kang, B. Tang, and M. Pecht, "Deep residual networks with dynamically weighted wavelet coefficients for fault diagnosis of planetary gearboxes," IEEE Transactions on Industrial Electronics, vol. 65, no. 5, pp. 4290-4300, 2017.</p>
<p dir="ltr">[10] Z. Zhao et al., "Deep learning algorithms for rotating machinery intelligent diagnosis: An open source benchmark study," ISA Transactions, vol. 107, pp. 224-255, 2020.</p>
<p dir="ltr">[11] H. Zhao, S. Sun, and B. Jin, "Sequential fault diagnosis based on LSTM neural network," Ieee Access, vol. 6, pp. 12929-12939, 2018.</p>
<p dir="ltr">[12] M. Yuan, Y. Wu, and L. Lin, "Fault diagnosis and remaining useful life estimation of aero engine using LSTM neural network," in 2016 IEEE international conference on aircraft utility systems (AUS), 2016: IEEE, pp. 135- 140.</p>
<p dir="ltr">[13] K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770-778.</p>
<p dir="ltr">[14] Y. Jin, C. Qin, Y. Huang, and C. Liu, "Actual bearing compound fault diagnosis based on active learning and decoupling attentional residual network," Measurement, vol. 173, p. 108500, 2021.</p>
<p dir="ltr">[15] S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural computation, vol. 9, no. 8, pp. 1735-1780, 1997</p>