RESEARCH PAPER
Rolling Bearing Fault Diagnosis Method Based on RSBU-MSCNN under Strong Background Noise
1
School of Mechanical Engineering, Hebei University of Science and Technology, Shijiazhuang, 050018, P.R., China
2
Center of Research and Development, KINGYEE(Beijing) Co., LTD., Beijing 100024, China
A – Conceptualization; B – Methodology; C – Software; D – Validation; E – Formal analysis; F – Investigation; G – Resources; H – Data curation; I – Writing – original draft; J – Writing – review & editing; K – Visualization; L – Supervision; M – Project administration; N – Funding acquisition
Submission date: 2025-12-29
Final revision date: 2026-03-17
Acceptance date: 2026-04-21
Online publication date: 2026-07-03
Corresponding author
Zhe Wu
School of Mechanical Engineering, Hebei University of Science and Technology, Shijiazhuang, 050018, P.R., China
School of Mechanical Engineering, Hebei University of Science and Technology, Shijiazhuang, 050018, P.R., China
KEYWORDS
TOPICS
ABSTRACT
Rolling bearings are key elements in rotating machinery, and reliable fault diagnosis is crucial for condition monitoring and maintenance decisions. Under strong background noise, vibration signals are easily distorted, which degrades conventional CNN-based diagnosis. To address this issue, an RSBU-MSCNN-based approach is proposed. First, Gaussian white noise with different signal-to-noise ratios is added to original signals to simulate industrial noise, and one-dimensional vibration signals are transformed into two-dimensional time–frequency representations using CWT. Then, a residual shrinkage module with a soft-threshold function is introduced for adaptive denoising and redundant noise suppression, while multi-channel, multi-scale convolutions enhance robust feature extraction across different receptive fields. Finally, faults are classified using fully connected layers. Experiments on multiple datasets show high accuracy under strong noise, confirming the robustness and applicability of the proposed method for industrial maintenance.
REFERENCES (45)
1.
Zhang Y, Ji J, Ren Z, Ni Q, Gu F, Feng K, Yu K, Ge J, Lei Z, Liu Z. Digital twin-driven partial domain adaptation network for intelligent fault diagnosis of rolling bearing. Reliability Engineering & System Safety 2023; 234: 109186. https://doi.org/10.1016/j.ress....
2.
Wu H, Li J, Zhang Q, Tao J, Meng Z. Intelligent fault diagnosis of rolling bearings under varying operating conditions based on domain-adversarial neural network and attention mechanism. ISA Transactions 2022; 130: 477-489. https://doi.org/10.1016/j.isat....
3.
Xu Y, Li Z, Wang S, Li W, Sarkodie-Gyan T, Feng S. A hybrid deep-learning model for fault diagnosis of rolling bearings. Measurement 2021; 169: 108502. https://doi.org/10.1016/j.meas....
4.
Wu D, Guan H, Zhao H. Parameterized Iterative Time–Frequency-Multisqueezing Transform for Bearing Fault Diagnosis. IEEE Transactions on Instrumentation and Measurement 2025; 74: 1-11. https://doi.org/10.1109/tim.20....
5.
Zhong J, Lin C, Gao Y, Zhong J, Zhong S. Fault diagnosis of rolling bearings under variable conditions based on unsupervised domain adaptation method. Mechanical Systems and Signal Processing 2024; 215: 111430. https://doi.org/10.1016/j.ymss....
6.
Cheng J, Yang Y, Shao H, Pan H, Zheng J, Cheng J. Enhanced periodic mode decomposition and its application to composite fault diagnosis of rolling bearings. ISA Transactions 2022; 125: 474-491. https://doi.org/10.1016/j.isat....
7.
Gao Y, Yu D. Intelligent fault diagnosis for rolling bearings based on graph shift regularization with directed graphs. Advanced Engineering Informatics 2021; 47: 101253. https://doi.org/10.1016/j.aei.....
8.
Yu M, Zhang Y, Yang C. Rolling bearing faults identification based on multiscale singular value. Advanced Engineering Informatics 2023; 57: 102040. https://doi.org/10.1016/j.aei.....
9.
Cai B, Zhang L, Tang G. Encogram: An autonomous weak transient fault enhancement strategy and its application in bearing fault diagnosis. Measurement 2023; 206: 112333. https://doi.org/10.1016/j.meas....
10.
Ni Q, Ji J C, Halkon B, Feng K, Nandi A K. Physics-Informed Residual Network (PIResNet) for rolling element bearing fault diagnostics. Mechanical Systems and Signal Processing 2023; 200: 110544. https://doi.org/10.1016/j.ymss....
11.
Chen S, Zheng W, Xiao H, Han P, Luo K. A residual convolution transfer framework based on slow feature for cross-domain machinery fault diagnosis. Neurocomputing 2023; 546: 126322. https://doi.org/10.1016/j.neuc....
12.
Zhao H, Liu C, Dang X, Xu J, Deng W. Few-Shot Cross-Domain Fault Diagnosis of Transportation Motor Bearings Using MAML-GA. IEEE Transactions on Transportation Electrification 2026; 12(1): 1165-1174. https://doi.org/10.1109/tte.20....
13.
Li J, Deng W, Ding J, Zhao H. IBN-MixStyle Network With Dynamic Weighted Invariant Risk Minimization for Domain-Generalized Bearing Fault Diagnosis. IEEE Transactions on Consumer Electronics 2025; 71(4): 9929-9939. https://doi.org/10.1109/tce.20....
14.
Huang C, Peng Y, Deng W. A dendrite net learning multi-objective artificial bee colony algorithm for UAV. Applied Soft Computing 2026; 189: 114449. https://doi.org/10.1016/j.asoc....
15.
Deng W, Li X, Sun Y, Zhao H. Privacy Protection-Enhanced Vertical-Horizontal Federated Learning Secure Sharing for Multisource Heterogeneous Data. IEEE Transactions on Industrial Informatics 2026; 1-10. https://doi.org/10.1109/tii.20....
16.
Dao F, Zeng Y, Qian J. Fault diagnosis of hydro-turbine via the incorporation of bayesian algorithm optimized CNN-LSTM neural network. Energy 2024; 290: 130326. https://doi.org/10.1016/j.ener....
17.
Guo Z, Yang M, Huang X. Bearing fault diagnosis based on speed signal and CNN model. Energy Reports 2022; 8: 904-913. https://doi.org/10.1016/j.egyr....
18.
Wang H, Xu J, Yan R, Gao R X. A New Intelligent Bearing Fault Diagnosis Method Using SDP Representation and SE-CNN. IEEE Transactions on Instrumentation and Measurement 2020; 69(5): 2377-2389. https://doi.org/10.1109/tim.20....
19.
Gu J, Peng Y, Lu H, Chang X, Chen G. A novel fault diagnosis method of rotating machinery via VMD, CWT and improved CNN. Measurement 2022; 200: 111635. https://doi.org/10.1016/j.meas....
20.
Sinitsin V, Ibryaeva O, Sakovskaya V, Eremeeva V. Intelligent bearing fault diagnosis method combining mixed input and hybrid CNN-MLP model. Mechanical Systems and Signal Processing 2022; 180: 109454. https://doi.org/10.1016/j.ymss....
21.
Deng W, Li H, Zhao H. Antinoise Bearing Fault Diagnosis Using Time-Reassigned Multisynchrosqueezing Transform and Complex Sparse Learning Dictionary. IEEE Transactions on Instrumentation and Measurement 2025; 74: 1-10. https://doi.org/10.1109/tim.20....
22.
Wang X, Mao D, Li X. Bearing fault diagnosis based on vibro-acoustic data fusion and 1D-CNN network. Measurement 2021; 173: 108518. https://doi.org/10.1016/j.meas....
23.
Han Y, Zhang F, Li Z, Wang Q, Li C, Lai P, Li T, Teng F, Jin Z. MT-ConvFormer: A Multitask Bearing Fault Diagnosis Method Using a Combination of CNN and Transformer. IEEE Transactions on Instrumentation and Measurement 2025; 74: 1-16. https://doi.org/10.1109/tim.20....
24.
Wang H, Liu Z, Peng D, Cheng Z. Attention-guided joint learning CNN with noise robustness for bearing fault diagnosis and vibration signal denoising. ISA Transactions 2022; 128: 470-484. https://doi.org/10.1016/j.isat....
25.
Han T, Ma R, Zheng J. Combination bidirectional long short-term memory and capsule network for rotating machinery fault diagnosis. Measurement 2021; 176: 109208. https://doi.org/10.1016/j.meas....
26.
Zhao K, Xiao J, Li C, Xu Z, Yue M. Fault diagnosis of rolling bearing using CNN and PCA fractal based feature extraction. Measurement 2023; 223: 113754. https://doi.org/10.1016/j.meas....
27.
Peng S, Xing J, Liu X. A Rolling Bearing Vibration Signal Noise Reduction Processing Algorithm Using the Fusion HPO-VMD and Improved Wavelet Threshold. Symmetry 2025; 17(8): 1316. https://doi.org/10.3390/sym170....
28.
Qiu Z, Fan S, Liang H, Liu J. Multimodal fusion fault diagnosis method under noise interference. Applied Acoustics 2025; 228: 110301. https://doi.org/10.1016/j.apac....
29.
Du Y, Geng X, Zhou Q, Cheng S. A fault diagnosis method for offshore wind turbine bearing based on adaptive deep echo state network and bidirectional long short term memory network in noisy environment. Ocean Engineering 2024; 312: 119101. https://doi.org/10.1016/j.ocea....
30.
Li D, Li M, Yang L, Wang X, Zhang F, Liang Y. Rolling bearing fault diagnosis in strong noise background based on vibration signals. Signal, Image and Video Processing 2023; 18(2): 1295-1303. https://doi.org/10.1007/s11760....
31.
Xiao B, Zhao Y, Zhou C, Ou J, Huang G. A noise-robust CNN architecture with global attention and gated convolutional Kernels for bearing fault detection. Measurement Science and Technology 2024; 35(8): 086142. https://doi.org/10.1088/1361-6....
32.
Chen Z, Wang Y, Wu J, Deng C, Hu K. Sensor data-driven structural damage detection based on deep convolutional neural networks and continuous wavelet transform. Applied Intelligence 2021; 51(8): 5598-5609. https://doi.org/10.1007/s10489....
33.
Cheng Y, Lin M, Wu J, Zhu H, Shao X. Intelligent fault diagnosis of rotating machinery based on continuous wavelet transform local binary convolutional neural network. Knowledge-Based Systems 2021; 216: 106796. https://doi.org/10.1016/j.knos....
34.
Belaid K, Miloudi A, Bournine H. The processing of resonances excited by gear faults using continuous wavelet transform with adaptive complex Morlet wavelet and sparsity measurement. Measurement 2021; 180: 109576. https://doi.org/10.1016/j.meas....
35.
Huang Y J, Liao A H, Hu D Y, Shi W, Zheng S B. Multi-scale convolutional network with channel attention mechanism for rolling bearing fault diagnosis. Measurement 2022; 203: 111935. https://doi.org/10.1016/j.meas....
36.
Tang S, Zhu Y, Yuan S. Intelligent fault diagnosis of hydraulic piston pump based on deep learning and Bayesian optimization. ISA Transactions 2022; 129: 555-563. https://doi.org/10.1016/j.isat....
37.
Li J, Lin M, Li Y, Wang X. Transfer learning network for nuclear power plant fault diagnosis with unlabeled data under varying operating conditions. Energy 2022; 254: 124358. https://doi.org/10.1016/j.ener....
38.
Zhao M, Zhong S, Fu X, Tang B, Pecht M. Deep Residual Shrinkage Networks for Fault Diagnosis. IEEE Transactions on Industrial Informatics 2020; 16(7): 4681-4690. https://doi.org/10.1109/tii.20....
39.
Lv X, Wang J, Qin R, Bao J, Jiang X, Zhang Z, Han B, Jiang X. Self-learning guided residual shrinkage network for intelligent fault diagnosis of planetary gearbox. Engineering Applications of Artificial Intelligence 2025; 139: 109603. https://doi.org/10.1016/j.enga....
40.
Zhan F, Hu L, Huang W, Dong Y, He H, Wu G. Category knowledge-guided few-shot bearing fault diagnosis. Engineering Applications of Artificial Intelligence 2025; 139: 109489. https://doi.org/10.1016/j.enga....
41.
He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition 2016; 770-778. https://doi.org/10.1109/CVPR.2....
42.
Liu Z, Mao H, Wu C Y, Feichtenhofer C, Darrell T, Xie S. A convnet for the 2020s. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition 2022; 11976-11986. https://doi.org/10.1109/CVPR52....
43.
Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks. Communications of the ACM 2017; 60(6): 84-90. https://doi.org/10.1145/306538....
44.
Zhang W, Li C, Peng G, Chen Y, Zhang Z. 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 2018; 100: 439-453. https://doi.org/10.1016/j.ymss....
45.
Vincent P, Larochelle H, Bengio Y, Manzagol P A. Extracting and composing robust features with denoising autoencoders. Proceedings of the 25th international conference on Machine learning 2008; 1096-1103. https://doi.org/10.1145/139015....
Share
RELATED ARTICLE
| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
