A Novel Intelligent Fault Diagnosis Method for Bearings with Multi-Source Data and Improved GASA
Abstract
:1. Introduction
2. Theoretical Background
2.1. Convolutional Neural Networks
2.2. Genetic Algorithm
2.3. Simulated Annealing Algorithm
3. The Proposed Method
3.1. GASA
3.2. The Structure of the Model
3.3. The Fault Diagnosis Process Using GASA-MSCNN
4. Experimental Validation
4.1. Dataset Description
4.2. Performance Analysis of the Proposed Method
4.3. Diagnosis Results under Different Noise Levels
4.4. Comparative Experiments among Different Methods
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Zhao, D.; Li, J.; Cheng, W.; Wen, W. Bearing multi-fault diagnosis with iterative generalized demodulation guided by enhanced rotational frequency matching under time-varying speed conditions. ISA Trans. 2023, 133, 518–528. [Google Scholar] [CrossRef]
- Weng, C.; Lu, B.; Gu, Q.; Zhao, X. A novel hierarchical transferable network for rolling bearing fault diagnosis under variable working conditions. Nonlinear Dyn. 2023, 111, 11315–11334. [Google Scholar] [CrossRef]
- Wen, L.; Li, X.; Gao, L.; Zhang, Y. A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method. IEEE Trans. Ind. Electron. 2018, 65, 5990–5998. [Google Scholar] [CrossRef]
- Jiao, J.; Zhao, M.; Lin, J.; Liang, K. A comprehensive review on convolutional neural network in machine fault diagnosis. Neurocomputing 2020, 417, 36–63. [Google Scholar] [CrossRef]
- Gao, X.; Wei, H.; Li, T.; Yang, G. A rolling bearing fault diagnosis method based on LSSVM. Adv. Mech. Eng. 2020, 12, 1687814019899561. [Google Scholar] [CrossRef]
- Pu, H.; Zhang, K.; An, Y. Restricted Sparse Networks for Rolling Bearing Fault Diagnosis. IEEE Trans. Ind. Inform. 2023, 19, 11139–11149. [Google Scholar] [CrossRef]
- Li, Y.; Song, L.; Sun, Q.; Xu, H.; Li, X.; Fang, Z.; Yao, W. Rolling bearing fault diagnosis based on quantum LS-SVM. EPJ Quantum Technol. 2022, 9, 18. [Google Scholar] [CrossRef]
- Lei, C.; Miao, C.; Wan, H.; Zhou, J.; Hao, D.; Feng, R. Rolling bearing fault diagnosis method based on MTF-MFACNN. Meas. Sci. Technol. 2023, 35, 035007. [Google Scholar] [CrossRef]
- Ma, S.; Yuan, Y.; Wu, J.; Jiang, Y.; Jia, B.; Li, W. Multisensor Decision Approach for HVCB Fault Detection Based on the Vibration Information. IEEE Sens. J. 2021, 21, 985–994. [Google Scholar] [CrossRef]
- Kou, L.; Qin, Y.; Zhao, X.; Chen, X.A. A Multi-Dimension End-to-End CNN Model for Rotating Devices Fault Diagnosis on High-Speed Train Bogie. IEEE Trans. Veh. Technol. 2020, 69, 2513–2524. [Google Scholar] [CrossRef]
- Zhou, K.; Lu, N.; Jiang, B. Information Fusion-Based Fault Diagnosis Method Using Synthetic Indicator. IEEE Sens. J. 2023, 23, 5124–5133. [Google Scholar] [CrossRef]
- Praveen Kumar, T.; Saimurugan, M.; Hari Haran, R.B.; Siddharth, S.; Ramachandran, K.I. A multi-sensor information fusion for fault diagnosis of a gearbox utilizing discrete wavelet features. Meas. Sci. Technol. 2019, 30, 085101. [Google Scholar] [CrossRef]
- Wu, Z.; Zhang, Q.; Cheng, L.; Tan, S. A New Method of Two-stage Planetary Gearbox Fault Detection Based on Multi-Sensor Information Fusion. Appl. Sci. 2019, 9, 5443. [Google Scholar] [CrossRef]
- Tong, J.; Liu, C.; Zheng, J.; Pan, H. Multi-sensor information fusion and coordinate attention-based fault diagnosis method and its interpretability research. Eng. Appl. Artif. Intell. 2023, 124, 106614. [Google Scholar] [CrossRef]
- Liu, C.; Tong, J.; Zheng, J.; Pan, H.; Bao, J. Rolling bearing fault diagnosis method based on multi-sensor two-stage fusion. Meas. Sci. Technol. 2022, 33, 125105. [Google Scholar] [CrossRef]
- Wang, Z.; Wu, Z.; Li, X.; Shao, H.; Han, T.; Xie, M. Attention-aware temporal–spatial graph neural network with multi-sensor information fusion for fault diagnosis. Knowl.-Based Syst. 2023, 278, 110891. [Google Scholar] [CrossRef]
- Yu, K.; Lin, T.R.; Tan, J.; Ma, H. An adaptive sensitive frequency band selection method for empirical wavelet transform and its application in bearing fault diagnosis. Measurement 2019, 134, 375–384. [Google Scholar] [CrossRef]
- Kannan, V.; Li, H.; Dao, D.V. Demodulation Band Optimization in Envelope Analysis for Fault Diagnosis of Rolling Element Bearings Using a Real-Coded Genetic Algorithm. IEEE Access 2019, 7, 168828–168838. [Google Scholar] [CrossRef]
- Martin-Diaz, I.; Morinigo-Sotelo, D.; Duque-Perez, O.; Osornio-Rios, R.A.; Romero-Troncoso, R.J. Hybrid algorithmic approach oriented to incipient rotor fault diagnosis on induction motors. ISA Trans. 2018, 80, 427–438. [Google Scholar] [CrossRef]
- Liu, X.; Wu, R.; Wang, R.; Zhou, F.; Chen, Z.; Guo, N. Bearing fault diagnosis based on particle swarm optimization fusion convolutional neural network. Front. Neurorobot. 2022, 16, 1044965. [Google Scholar] [CrossRef]
- Chen, J.; Xu, Q.; Xue, X.; Guo, Y.; Chen, R. Quantum-behaved particle swarm optimization of convolutional neural network for fault diagnosis. J. Exp. Theor. Artif. Intell. 2022, 1–17. [Google Scholar] [CrossRef]
- Rajagopalan, S.; Singh, J.; Purohit, A. Performance analysis of genetically optimized 1D-convolutional neural network architecture for rotor system fault detection and diagnosis. Proc. Inst. Mech. Eng. Part E J. Process Mech. Eng. 2024. [Google Scholar] [CrossRef]
- He, F.; Ye, Q. A Bearing Fault Diagnosis Method Based on Wavelet Packet Transform and Convolutional Neural Network Optimized by Simulated Annealing Algorithm. Sensors 2022, 22, 1410. [Google Scholar] [CrossRef]
- Bai, R.; Xu, Q.; Meng, Z.; Cao, L.; Xing, K.; Fan, F. Rolling bearing fault diagnosis based on multi-channel convolution neural network and multi-scale clipping fusion data augmentation. Measurement 2021, 184, 109885. [Google Scholar] [CrossRef]
- Cerrada, M.; Zurita, G.; Cabrera, D.; Sánchez, R.-V.; Artés, M.; Li, C. Fault diagnosis in spur gears based on genetic algorithm and random forest. Mech. Syst. Signal Process. 2016, 70–71, 87–103. [Google Scholar] [CrossRef]
- Boudiaf, R.; Abdelkarim, B.; Issam, H. Bearing fault diagnosis in induction motor using continuous wavelet transform and convolutional neural networks. Int. J. Power Electron. Drive Syst. (IJPEDS) 2024, 15, 591–602. [Google Scholar] [CrossRef]
- Dong, Z.; Zhao, D.; Cui, L. An Intelligent Bearing Fault Diagnosis Framework: One Dimensional Improved Self Attention-enhanced CNN and Empirical Wavelet Transform. Nonlinear Dyn. 2024, 112, 6439–6459. [Google Scholar] [CrossRef]
- Fu, W.; Jiang, X.; Li, B.; Tan, C.; Chen, B.; Chen, X. Rolling bearing fault diagnosis based on 2D time-frequency images and data augmentation technique. Meas. Sci. Technol. 2023, 34, 045005. [Google Scholar] [CrossRef]
- Huang, G.; Zhang, Y.; Ou, J. Transfer remaining useful life estimation of bearing using depth-wise separable convolution recurrent network. Measurement 2021, 176, 109090. [Google Scholar] [CrossRef]
- Alyasseri, Z.A.A.; Khader, A.T.; Al-Betar, M.A.; Abasi, A.K.; Makhadmeh, S.N. EEG Signals Denoising Using Optimal Wavelet Transform Hybridized with Efficient Metaheuristic Methods. IEEE Access 2020, 8, 10584–10605. [Google Scholar] [CrossRef]
- Yao, D.; Yang, J.; Li, X.; Zhao, C. A Hybrid Approach for Fault Diagnosis of Railway Rolling Bearings Using STWD-EMD-GA-LSSVM. Math. Probl. Eng. 2016, 2016, 1–7. [Google Scholar] [CrossRef]
- Wang, D.; Guo, W.; Wang, X. A joint sparse wavelet coefficient extraction and adaptive noise reduction method in recovery of weak bearing fault features from a multi-component signal mixture. Appl. Soft Comput. 2013, 13, 4097–4104. [Google Scholar] [CrossRef]
- Xiong, J.; Liu, X.; Zhu, X.; Zhu, H.; Li, H.; Zhang, Q. Semi-Supervised Fuzzy C-Means Clustering Optimized by Simulated Annealing and Genetic Algorithm for Fault Diagnosis of Bearings. IEEE Access 2020, 8, 181976–181987. [Google Scholar] [CrossRef]
- Wang, B.; Lei, Y.; Li, N.; Yan, T. Deep separable convolutional network for remaining useful life prediction of machinery. Mech. Syst. Signal Process. 2019, 134, 106330. [Google Scholar] [CrossRef]
- Jamil, M.A.; Khanam, S. Influence of One-Way ANOVA and Kruskal–Wallis Based Feature Ranking on the Performance of ML Classifiers for Bearing Fault Diagnosis. J. Vib. Eng. Technol. 2023, 12, 3101–3132. [Google Scholar] [CrossRef]
Number | Rotation Speed (rpm) | Radial Force (N) | Load Torque (N/m) | Working Condition |
---|---|---|---|---|
0 | 1500 | 1000 | 0.7 | N_15_M07_F10 |
1 | 900 | 1000 | 0.7 | N_09_M07_F10 |
2 | 1500 | 1000 | 0.1 | N_15_M01_F10 |
3 | 1500 | 400 | 0.7 | N_15_M07_F14 |
Bearing Number | Damage | Location | Damage Level | Label |
---|---|---|---|---|
KA01 | EDM | OR | 1 | 0 |
KA04 | Fatigue: pitting | OR | 1 | 1 |
KA05 | Electric Engraver | OR | 1 | 2 |
KA06 | Electric Engraver | OR | 2 | 3 |
KA09 | Drilled | OR | 2 | 4 |
KI01 | EDM | IR | 1 | 5 |
KI03 | Electric Engraver | IR | 1 | 6 |
KI07 | Electric Engraver | IR | 2 | 7 |
KI18 | Fatigue: pitting | IR | 2 | 8 |
KI21 | Fatigue: pitting | IR | 1 | 9 |
Batch_Size | Learning Rate | |||||
---|---|---|---|---|---|---|
0.001 | 0.0001 | 0.0002 | 0.0004 | 0.0006 | 0.0008 | |
8 | 93.71% | 96.18% | 91.59% | 94.99% | 95.08% | 95.72% |
16 | 93.73% | 90.69% | 95.09% | 93.62% | 95.1% | 92.8% |
32 | 89.63% | 86.18% | 90.85% | 92.26% | 91.52% | 90.7% |
64 | 86.95% | 75.37% | 83.38% | 84.15% | 83.62% | 87.22% |
128 | 72.53% | 56.01% | 64.85% | 66.77% | 70.48% | 72.38% |
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Hu, Q.; Fu, X.; Guan, Y.; Wu, Q.; Liu, S. A Novel Intelligent Fault Diagnosis Method for Bearings with Multi-Source Data and Improved GASA. Sensors 2024, 24, 5285. https://doi.org/10.3390/s24165285
Hu Q, Fu X, Guan Y, Wu Q, Liu S. A Novel Intelligent Fault Diagnosis Method for Bearings with Multi-Source Data and Improved GASA. Sensors. 2024; 24(16):5285. https://doi.org/10.3390/s24165285
Chicago/Turabian StyleHu, Qingming, Xinjie Fu, Yanqi Guan, Qingtao Wu, and Shang Liu. 2024. "A Novel Intelligent Fault Diagnosis Method for Bearings with Multi-Source Data and Improved GASA" Sensors 24, no. 16: 5285. https://doi.org/10.3390/s24165285