Hostname: page-component-848d4c4894-wg55d Total loading time: 0 Render date: 2024-06-01T11:06:12.081Z Has data issue: false hasContentIssue false
  • English
  • Français

A novel intelligent fault diagnosis method of bearing based on multi-head self-attention convolutional neural network

Published online by Cambridge University Press:  17 May 2024

Hang Ren ,
Shaogang Liu ,
Bo Qiu ,
Hong Guo  and
Dan Zhao Open the ORCID record for Dan Zhao [Opens in a new window]
Hang Ren
Affiliation:
College of Mechatronic Engineering, Harbin Engineering University, Harbin 150001, China
Shaogang Liu
Affiliation:
College of Mechatronic Engineering, Harbin Engineering University, Harbin 150001, China
Bo Qiu
Affiliation:
CSSC Fire Equipment Co., Ltd, Jiangxi 332000, China
Hong Guo
Affiliation:
College of Mechatronic Engineering, Harbin Engineering University, Harbin 150001, China
Dan Zhao*
Affiliation:
College of Mechatronic Engineering, Harbin Engineering University, Harbin 150001, China
*
Corresponding author: Dan Zhao; Email: heuzhaodan@outlook.com
Get access Rights & Permissions [Opens in a new window]

Abstract

Deep learning (DL) has been widely used in bearing fault diagnosis. In particular, convolutional neural networks (CNNs) improve diagnosis accuracy by extracting excellent fault features. However, CNN lacks an explicit learning mechanism to distinguish between different fault characteristics in the input signal to the diagnosis results. This article presents a new end-to-end depth framework called multi-head self-attention convolution neural network (MSA-CNN) for bearing fault diagnosis. Firstly, we adopt a data pre-processing method that directly converts one-dimensional (1D) original signals into two-dimensional (2D) grayscale images, which is simple to implement and preserves the complete information of the original signal. Secondly, multi-head self-attention (MSA) is first constructed to aggregate the global information and adaptively assign weights to the input signal's features. Thirdly, the CNN with small-scale kernels extracted detailed local features. Finally, the learned high-level representations are fed into the full connect (FC) layer for fault diagnosis. The performance of the MSA-CNN is validated on different datasets. The results show that the proposed MSA-CNN can significantly improve fault diagnosis accuracy compared with the other state-of-the-art methods and has excellent noise immunity performance.

Keywords

bearing fault diagnosisconvolutional neural networkdeep learningglobal informationmulti-head self-attention mechanism
Type
Research Article
Information
AI EDAM , Volume 38 , 2024 , e9
Check for updates
Copyright
Copyright © The Author(s), 2024. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abdeljaber, O, Avci, O, Kiranyaz, S, Gabbouj, M and Inman, DJ (2017) Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks. Journal of Sound and Vibration 388, 154–170.CrossRefGoogle Scholar
Chen, Y, Zhang, D, Zhang, H and Wang, Q-G (2022) Dual-path mixed-domain residual threshold networks for bearing fault diagnosis. IEEE Transactions on Industrial Electronics 69, 13462–13472.CrossRefGoogle Scholar
Ding, X and He, Q (2017) Energy-fluctuated multiscale feature learning with deep ConvNet for intelligent spindle bearing fault diagnosis. IEEE Transactions on Instrumentation and Measurement 66, 1926–1935.CrossRefGoogle Scholar
Dosovitskiy, A, Beyer, L, Kolesnikov, A, Weissenborn, D, Zhai, X, Unterthiner, T, Dehghani, M, Minderer, M, Heigold, G, Gelly, S, Uszkoreit, J and Houlsby, N (2021) An image is worth 16X16 words: transformers for image recognition at scale. International Conference on Learning Representations.Google Scholar
Hao, S, Ge, F-X, Li, Y and Jiang, J (2020) Multisensor bearing fault diagnosis based on one-dimensional convolutional long short-term memory networks. Measurement 159, 107802.CrossRefGoogle Scholar
He, M and He, D (2020) A new hybrid deep signal processing approach for bearing fault diagnosis using vibration signals. Neurocomputing 396, 542–555.CrossRefGoogle Scholar
He, K, Zhang, X, Ren, S and Sun, J (2016) Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778.CrossRefGoogle Scholar
Huang, W, Cheng, J, Yang, Y and Guo, G (2019) An improved deep convolutional neural network with multi-scale information for bearing fault diagnosis. Neurocomputing 359, 77–92.CrossRefGoogle Scholar
Lei, Y, Yang, B, Jiang, X, Jia, F, Li, N and Nandi, AK (2020) Applications of machine learning to machine fault diagnosis: a review and roadmap. Mechanical Systems and Signal Processing 138, 106587.CrossRefGoogle Scholar
Li, K, Xiong, M, Li, F, Su, L and Wu, J (2019 a) A novel fault diagnosis algorithm for rotating machinery based on a sparsity and neighborhood preserving deep extreme learning machine. Neurocomputing 350, 261–270.CrossRefGoogle Scholar
Li, R, Wu, Z, Jia, J, Zhao, S and Meng, H (2019 b) Dilated residual network with multi-head self-attention for speech emotion recognition. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6675–6679.CrossRefGoogle Scholar
Li, M, Zhang, J, Song, J, Li, Z and Lu, S (2023) A clinical-oriented non-severe depression diagnosis method based on cognitive behavior of emotional conflict. IEEE Transactions on Computational Social Systems 10, 131–141.CrossRefGoogle Scholar
Liang, P, Deng, C, Wu, J and Yang, Z (2020) Intelligent fault diagnosis of rotating machinery via wavelet transform, generative adversarial nets and convolutional neural network. Measurement 159, 107768.CrossRefGoogle Scholar
Liu, R, Yang, B, Zio, E and Chen, X (2018) Artificial intelligence for fault diagnosis of rotating machinery: a review. Mechanical Systems and Signal Processing 108, 33–47.CrossRefGoogle Scholar
Lu, C, Wang, Y, Ragulskis, M and Cheng, Y (2016) Fault diagnosis for rotating machinery: a method based on image processing. PLoS One 11, e0164111.CrossRefGoogle ScholarPubMed
Manikandan, S and Duraivelu, K (2021) Fault diagnosis of various rotating equipment using machine learning approaches – a review. Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering 235, 629–642.CrossRefGoogle Scholar
Qin, C, Huang, G, Yu, H, Wu, R, Tao, J and Liu, C (2023) Geological information prediction for shield machine using an enhanced multi-head self-attention convolution neural network with two-stage feature extraction. Geoscience Frontiers 14, 101519.CrossRefGoogle Scholar
Shao, S, McAleer, S, Yan, R and Baldi, P (2019) Highly accurate machine fault diagnosis using deep transfer learning. IEEE Transactions on Industrial Informatics 15, 2446–2455.CrossRefGoogle Scholar
Smith, WA and Randall, RB (2015) Rolling element bearing diagnostics using the Case Western Reserve University data: a benchmark study. Mechanical Systems and Signal Processing 64–65, 100–131.CrossRefGoogle Scholar
Wang, H, Xu, J, Yan, R, Sun, C and Chen, X (2020) Intelligent bearing fault diagnosis using multi-head attention-based CNN. Procedia Manufacturing 49, 112–118.CrossRefGoogle Scholar
Wen, L, Li, X and Gao, L (2018) A new convolutional neural network-based data-driven fault diagnosis method. IEEE Transactions on Industrial Electronics 65, 5990–5998.CrossRefGoogle Scholar
Xiao, X, Zhang, D, Hu, G, Jiang, Y and Xia, S (2020) CNN-MHSA: a convolutional neural network and multi-head self-attention combined approach for detecting phishing websites. Neural Networks 125, 303–312.CrossRefGoogle ScholarPubMed
Xiao, Y, Shao, H, Han, S, Huo, Z and Wan, J (2022) Novel joint transfer network for unsupervised bearing fault diagnosis from simulation domain to experimental domain. IEEE/ASME Transactions on Mechatronics 27, 5254–5263.CrossRefGoogle Scholar
Xie, C, Zhou, L, Ding, S, Liu, R and Zheng, S (2023) Experimental and numerical investigation on self-propulsion performance of polar merchant ship in brash ice channel. Ocean Engineering 269, 113424.CrossRefGoogle Scholar
Xue, F, Zhang, W, Xue, F, Li, D, Xie, S and Fleischer, J (2021) A novel intelligent fault diagnosis method of rolling bearing based on two-stream feature fusion convolutional neural network. Measurement 176, 109226.CrossRefGoogle Scholar
Zhang, W, Li, C, Peng, G, Chen, Y and Zhang, Z (2018) A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load. Mechanical Systems and Signal Processing 100, 439–453.CrossRefGoogle Scholar
Zhao, M, Zhong, S, Fu, X, Tang, B and Pecht, M (2020) Deep residual shrinkage networks for fault diagnosis. IEEE Transactions on Industrial Informatics 16, 4681–4690.CrossRefGoogle Scholar
Zhao, H, Liu, J, Chen, H, Chen, J, Li, Y, Xu, J and Deng, W (2022 a) Intelligent diagnosis using continuous wavelet transform and Gauss convolutional deep belief network. IEEE Transactions on Reliability 72, 692–702.CrossRefGoogle Scholar
Zhao, H, Yang, X, Chen, B, Chen, H and Deng, W (2022 b) Bearing fault diagnosis using transfer learning and optimized deep belief network. Measurement Science and Technology 33, 065009.CrossRefGoogle Scholar
Zhou, X, Cai, X, Zhang, H, Zhang, Z, Jin, T, Chen, H and Deng, W (2023) Multi-strategy competitive-cooperative co-evolutionary algorithm and its application. Information Sciences 635, 328–344.CrossRefGoogle Scholar