Citation: | CHEN Chuang, LI Xianfeng, SHI Jiantao, YUE Dongdong. Intelligent fault diagnosis method for rolling bearings based on flexible residual neural network[J]. Chinese Journal of Engineering, 2025, 47(3): 480-488. DOI: 10.13374/j.issn2095-9389.2024.06.24.006 |
Rolling bearings play a crucial role in rotating machinery, and their efficient operation is vital for the machine’s longevity and performance. In numerous real-world situations, diagnosing faults in rolling bearings presents significant challenges. Signals obtained from industrial applications often contain unavoidable noise, complicating analysis. Additionally, the intricate working conditions in actual operations can greatly influence bearing signal characteristics. Consequently, traditional diagnostic techniques struggle to effectively handle the effects of varying loads and noise. To improve the accuracy of fault diagnosis for rolling bearings in noisy and variable working conditions, a new approach using a flexible residual neural network (FResNet) is introduced. This network is built on a dynamic subtraction average-based optimizer (DSABO) and a parallel attention module (PAM). The core of FResNet is a flexible residual module based on convolutional neural networks, which allows for adjustments in the number of convolutional layers, convolutional kernels, and skip connections during optimization. These design features improve the network’s ability to extract fault features and prevent degradation. Second, a DSABO with a dynamic position update strategy is proposed for parameter optimization of the above FResNet with the flexible residual module. This optimizer helps the model avoid being trapped in local optima, strengthening the fault diagnosis performance of the network. Third, a PAM is integrated, featuring convolutional layers that combine channel and spatial attention. This integration enhances data feature extraction by aligning it with rolling bearing operation data. Together, DSABO, PAM, and FResNet create an effective rolling bearing fault diagnosis model known as DSABO–PAM–FResNet. Finally, the feasibility and effectiveness of the proposed DSABO–PAM–FResNet model are validated using the rolling bearing fault dataset from Case Western Reserve University in the United States. The ablation experiments reveal that the DSABO model consistently achieves accuracies above 97% across different noise environments. This performance surpasses that of models using grey wolf optimizer (GWO), butterfly optimization algorithm (BOA), and whale optimization algorithm (WOA), indicating the excellent search capabilities of the DSABO proposed in this paper. In noisy environments, the model incorporating the PAM module consistently achieves fault recognition accuracies above 97%. This performance exceeds that of models using the efficient channel attention module (ECAM) and spatial attention module (SAM), demonstrating PAM’s excellent capability to highlight fault signals. In challenging environments with a signal-to-noise ratio of –6 dB, the proposed model achieves a fault diagnosis accuracy of 97.18%, proving its strong noise resistance. Under different load conditions of 0.75 kW, 1.5 kW, and 2.25 kW the proposed model maintains an average accuracy of 98.2% in environments with a −4 dB signal-to-noise ratio. This demonstrates the model’s excellent adaptability to variable working conditions. Comparison results demonstrated that DSABO-PAM-FResNet outperforms other intelligent fault diagnosis methods in terms of diagnostic accuracy, providing a new and effective intelligent method for rolling bearing fault diagnosis.
[1] |
Chen C, Lu N Y, Jiang B, et al. Prediction interval estimation of aeroengine remaining useful life based on bidirectional long short-term memory network. IEEE Trans Instrum Meas, 2021, 70: 3527213
|
[2] |
蔡志鑫, 党章, 吕勇, 等. 自适应动模式分解和GA-SVM在行星轴承故障分类中的应用. 工程科学学报, 2023, 45(9):1559
Cai Z X, Dang Z, Lü Y, et al. Adaptive dynamic mode decomposition and GA-SVM with application to fault classification of planetary bearing. Chin J Eng, 2023, 45(9): 1559
|
[3] |
Chen C, Shi J T, Shen M Q, et al. Pseudo-label guided sparse deep belief network learning method for fault diagnosis of radar critical components. IEEE Trans Instrum Meas, 2023, 72: 3510212
|
[4] |
Xiao Y M, Shao H D, Han S Y, et al. Novel joint transfer network for unsupervised bearing fault diagnosis from simulation domain to experimental domain. IEEE/ASME Trans Mechatron, 2022, 27(6): 5254 doi: 10.1109/TMECH.2022.3177174
|
[5] |
欧洋, 郭正玉, 罗德林, 等. 基于图卷积深度强化学习的协同空战机动决策方法. 工程科学学报, 2024, 46(7):1227
Ou Y, Guo Z Y, Luo D L, et al. Collaborative air combat maneuvering decision-making method based on graph convolutional deep reinforcement learning. Chin J Eng, 2024, 46(7): 1227
|
[6] |
Chen C, Shi J T, Shen M Q, et al. A predictive maintenance strategy using deep learning quantile regression and kernel density estimation for failure prediction. IEEE Trans Instrum Meas, 2023, 72: 3506512
|
[7] |
陈闯, 李先锋, 史建涛. 基于深度学习的装备剩余寿命区间预测研究进展. 工程科学学报, 2024, 46(4):723
Chen C, Li X F, Shi J T. Research progress on remaining useful life interval prediction of equipment based on deep learning. Chin J Eng, 2024, 46(4): 723
|
[8] |
刘泽民, 程海勇, 毛明发, 等. 基于3D卷积神经网络的膏体屈服应力预测. 工程科学学报, 2024, 46(8):1337
Liu Z M, Cheng H Y, Mao M F, et al. Prediction of paste yield stress based on three-dimensional convolutional neural networks. Chin J Eng, 2024, 46(8): 1337
|
[9] |
Chen C, Tao G Y, Shi J T, et al. A lithium-ion battery degradation prediction model with uncertainty quantification for its predictive maintenance. IEEE Trans Ind Electron, 2024, 71(4): 3650 doi: 10.1109/TIE.2023.3274874
|
[10] |
卿粼波, 吴梦凡, 刘刚, 等. 基于小波域ADMM深度网络的图像复原算法. 工程科学与技术, 2022, 54(5):257
Qing L B, Wu M F, Liu G, et al. Deep ADMM network in wavelet domain for image restoration. Adv Eng Sci, 2022, 54(5): 257
|
[11] |
Xu Q S, Zhu B, Huo H B, et al. Fault diagnosis of rolling bearing based on online transfer convolutional neural network. Appl Acoust, 2022, 192: 108703 doi: 10.1016/j.apacoust.2022.108703
|
[12] |
Sinitsin V, Ibryaeva O, Sakovskaya V, et al. Intelligent bearing fault diagnosis method combining mixed input and hybrid CNN-MLP model. Mech Syst Signal Process, 2022, 180: 109454 doi: 10.1016/j.ymssp.2022.109454
|
[13] |
Ruan D W, Wang J, Yan J P, et al. CNN parameter design based on fault signal analysis and its application in bearing fault diagnosis. Adv Eng Inform, 2023, 55: 101877 doi: 10.1016/j.aei.2023.101877
|
[14] |
Ayas S, Ayas M S. A novel bearing fault diagnosis method using deep residual learning network. Multimed Tools Appl, 2022, 81(16): 22407 doi: 10.1007/s11042-021-11617-1
|
[15] |
Zhang Y, Liu W Y, Wang X, et al. A novel wind turbine fault diagnosis method based on compressed sensing and DTL-CNN. Renew Energy, 2022, 194: 249 doi: 10.1016/j.renene.2022.05.085
|
[16] |
Kong X G, Mao G, Wang Q B, et al. A multi-ensemble method based on deep auto-encoders for fault diagnosis of rolling bearings. Measurement, 2020, 151: 107132 doi: 10.1016/j.measurement.2019.107132
|
[17] |
Trojovský P, Dehghani M. Subtraction-average-based optimizer: A new swarm-inspired metaheuristic algorithm for solving optimization problems. Biomimetics (Basel), 2023, 8(2): 149 doi: 10.3390/biomimetics8020149
|
[18] |
Xu X Z, Li C, Zhang X L, et al. A dense ResNet model with RGB input mapping for cross-domain mechanical fault diagnosis. IEEE Instrum Meas Mag, 2023, 26(2): 40 doi: 10.1109/MIM.2023.10083021
|
[19] |
Chen C, Li X F, Shi J T. Hierarchical gray wolf optimizer-tuned flexible residual neural network with parallel attention module for bearing fault diagnosis. IEEE Sens J, 2024, 24(12): 19626 doi: 10.1109/JSEN.2024.3397656
|
[20] |
Mirjalili S, Mirjalili S M, Lewis A. Grey wolf optimizer. Adv Eng Softw, 2014, 69: 46 doi: 10.1016/j.advengsoft.2013.12.007
|
[21] |
Arora S, Singh S. Butterfly optimization algorithm: A novel approach for global optimization. Soft Comput, 2019, 23(3): 715 doi: 10.1007/s00500-018-3102-4
|
[22] |
Chakraborty S, Sharma S, Saha A K, et al. A novel improved whale optimization algorithm to solve numerical optimization and real-world applications. Artif Intell Rev, 2022, 55(6): 4605 doi: 10.1007/s10462-021-10114-z
|
[23] |
Singh P, Sharma A. Attention-based convolutional denoising autoencoder for two-lead ECG denoising and arrhythmia classification. IEEE Trans Instrum Meas, 2022, 71: 4007710
|
[24] |
Cao J W, Feng Y M, Zheng R Z, et al. Two-stream attention 3-D deep network-based childhood epilepsy syndrome classification. IEEE Trans Instrum Meas, 2022, 72: 2503412
|
[25] |
Wang H, Liu Z L, Peng D D, et al. Attention-guided joint learning CNN with noise robustness for bearing fault diagnosis and vibration signal denoising. ISA Trans, 2022, 128: 470 doi: 10.1016/j.isatra.2021.11.028
|
[26] |
Zhao B, Zhang X M, Li H, et al. Intelligent fault diagnosis of rolling bearings based on normalized CNN considering data imbalance and variable working conditions. Knowl Based Syst, 2020, 199: 105971 doi: 10.1016/j.knosys.2020.105971
|
[27] |
Shao S Y, McAleer S, Yan R Q, et al. Highly accurate machine fault diagnosis using deep transfer learning. IEEE Trans Ind Inform, 2019, 15(4): 2446 doi: 10.1109/TII.2018.2864759
|
[28] |
Chen Z Y, Gryllias K, Li W H. Intelligent fault diagnosis for rotary machinery using transferable convolutional neural network. IEEE Trans Ind Inform, 2020, 16(1): 339 doi: 10.1109/TII.2019.2917233
|
[29] |
Zhang J Q, Sun Y, Guo L, et al. A new bearing fault diagnosis method based on modified convolutional neural networks. Chin J Aeronaut, 2020, 33(2): 439 doi: 10.1016/j.cja.2019.07.011
|
[30] |
Lu C, Wang Z Y, Zhou B. Intelligent fault diagnosis of rolling bearing using hierarchical convolutional network based health state classification. Adv Eng Inform, 2017, 32: 139 doi: 10.1016/j.aei.2017.02.005
|
[31] |
Zhao J, Yang S P, Li Q, et al. A new bearing fault diagnosis method based on signal-to-image mapping and convolutional neural network. Measurement, 2021, 176: 109088 doi: 10.1016/j.measurement.2021.109088
|
[32] |
Xue F, Zhang W M, Xue F, et al. A novel intelligent fault diagnosis method of rolling bearing based on two-stream feature fusion convolutional neural network. Measurement, 2021, 176: 109226 doi: 10.1016/j.measurement.2021.109226
|