Tytuł artykułu
Autorzy
Treść / Zawartość
Pełne teksty:
Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
The fault diagnosis in rotating machinery is crucial for ensuring the safe and dependable operation of intricate mechanical systems. Addressing the limitations inherent in traditional deep learning approaches concerning extended time sequence encoding and subpar generalization capability is paramount. The study utilizes the Gramian Angular Field (GAF) and Squeeze and Excitation (SE) attention mechanisms to alleviate these constraints. GAF enhances feature extraction by emphasizing the angular relationships among adjacent signal points to uncover latent fault characteristics. Simultaneously, through the integration of SE with DenseNet architecture, the network facilitates global information exchange and improves multi-scale fusion, thereby enhancing the precise identification of fault type and location within the signal. Experiments conducted on two datasets achieved accuracies of 100% and 99.85%, respectively, outperforming other methods and models, thereby validating the effectiveness of this study.
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
art. no. 191445
Opis fizyczny
Bibliogr. 35 poz., rys., tab., wykr.
Twórcy
autor
- School of Mechanical Engineering, Xinjiang University, China
autor
- School of Mechanical Engineering, Xinjiang University, China
autor
- School of Mechanical Engineering, Xinjiang University, China
autor
- School of Mechanical Engineering, Xinjiang University, China
Bibliografia
- 1. Liu S, Ji Z, Wang Y, Zhang Z, Xu Z, Kan C, et al. Multi-feature fusion for fault diagnosis of rotating machinery based on convolutional neural network. Computer Communications. 2021;173:160-9. https://doi.org/10.1016/j.comcom.2021.04.016
- 2. Cen J, Yang ZH, Liu X, Xiong JB, Chen HH. A Review of Data-Driven Machinery Fault Diagnosis Using Machine Learning Algorithms. JOURNAL OF VIBRATION ENGINEERING & TECHNOLOGIES. 2022;10(7):2481-507. https://doi.org/10.1007/s42417-022-00498-9
- 3. Lei Y, Yang B, Jiang X, Jia F, Li N, Nandi AK. Applications of machine learning to machine fault diagnosis: A review and road map. Mechanical Systems and Signal Processing. 2020;138. https://doi.org/10.1016/j.ymssp.2019.106587
- 4. Chen X, Yang R, Xue Y, Huang M, Ferrero R, Wang Z. Deep Transfer Learning for Bearing Fault Diagnosis: A Systematic Review Since 2016. IEEE Transactions on Instrumentation and Measurement. 2023;72:1-21. https://doi.org/10.1109/TIM.2023.3244237
- 5. Ye M, Yan X, Jia M. Rolling Bearing Fault Diagnosis Based on VMD-MPE and PSO-SVM. Entropy. 2021;23(6). https://doi.org/10.3390/e23060762
- 6. Liu X, Tang B, Li Q, Yang Q. Twin prototype networks with noisy label self-correction for fault diagnosis of wind turbine gearboxes. Measurement Science and Technology. 2022;34(3):035006.https://doi.org/10.1088/1361-6501/aca3c3
- 7. Guo J, Si Z, Xiang J. Cycle kurtosis entropy guided symplectic geometry mode decomposition for detecting faults in rotating machinery. ISA Transactions. 2023;138:546-61. https://doi.org/10.1016/j.isatra.2023.03.026
- 8. Su Z, Wang F, Xiao H, Yu H, Dong S. A fault diagnosis model based on singular value manifold features, optimized SVMs and multi-sensor information fusion. Measurement Science and Technology. 2020;31(9):095002. https://doi.org/10.1088/1361-6501/ab842f
- 9. Li T, Peng Z, Xu H, He Q. Parameterized Domain Mapping for Order Tracking of Rotating Machinery. IEEE Transactions on Industrial Electronics. 2023;70(7):7406-16. https://doi.org/10.1109/TIE.2022.3201311
- 10. Li S, Li T, Sun C, Chen X, Yan R. WPConvNet: An Interpretable Wavelet Packet Kernel-Constrained Convolutional Network for Noise-Robust Fault Diagnosis. IEEE Transactions on Neural Networks and Learning Systems. 2023:1-15. https://doi.org/10.1109/TNNLS.2023.3282599
- 11. Aasi A, Tabatabaei R, Aasi E, Jafari SM. Experimental investigation on time-domain features in the diagnosis of rolling element bearings by acoustic emission. Journal of Vibration and Control. 2021;28(19-20):2585-95. doi: 0.1177/10775463211016130
- 12. Zhou Y, Sun W, Ye C, Peng B, Fang X, Lin C et al. Time-frequency Representation -enhanced Transfer Learning for Tool Condition Monitoring during milling of Inconel 718. Eksploatacja i Niezawodnosc – Maintenance and Reliability. 2023;25(2). https://doi.org/10.17531/ein/165926
- 13. Lei Y, Jia F, Lin J, Xing S, Ding SX. An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data. IEEE Transactions on Industrial Electronics. 2016;63(5):3137-47. https://doi.org/10.1109/TIE.2016.2519325
- 14. Zhang C, Zhang L. Wind turbine pitch bearing fault detection with Bayesian augmented temporal convolutional networks. Structural Health Monitoring. 2023. https://doi.org/10.1177/14759217231175886
- 15. Wang Q, Xu F. A novel rolling bearing fault diagnosis method based on Adaptive Denoising Convolutional Neural Network under noise background. Measurement. 2023;218. https://doi.org/10.1016/j.measurement.2023.113209
- 16. Wang H, Sun W, He L, Zhou J. Intelligent Fault Diagnosis Method for Gear Transmission Systems Based on Improved Multi-Scale Reverse Dispersion Entropy and Swarm Decomposition. IEEE Transactions on Instrumentation and Measurement. 2022;71:1-13. https://doi.org/10.1109/TIM.2021.3115207
- 17. Chen T, Zhang X, Wang C, Yu X, Wang S, Chen X. Domain adversarial neural network-based nonlinear system identification for helicopter transmission system. Nonlinear Dynamics. 2023;111(16):14695-711. https://doi.org/10.1007/s11071-023-08657-7
- 18. 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. https://doi.org/10.1016/j.knosys.2021.106796
- 19. Wang X, Mao D, Li X. Bearing fault diagnosis based on vibro-acoustic data fusion and 1D-CNN network. Measurement. 2021;173. https://doi.org/10.1016/j.measurement.2020.108518
- 20. Luo P, Yin Z, Yuan D, Gao F, Liu J. An Intelligent Method for Early Motor Bearing Fault Diagnosis Based on Wasserstein Distance Generative Adversarial Networks Meta Learning. IEEE Transactions on Instrumentation and Measurement. 2023;72:1-11. https://doi.org/10.1109/TIM.2023.3278289
- 21. Xia M, Li T, Xu L, Liu L, de Silva CW. Fault Diagnosis for Rotating Machinery Using Multiple Sensors and Convolutional Neural Networks. IEEE/ASME Transactions on Mechatronics. 2018;23(1):101-10. https://doi.org/10.1109/TMECH.2017.2728371
- 22. Chen Z, Mauricio A, Li W, Gryllias K. A deep learning method for bearing fault diagnosis based on Cyclic Spectral Coherence and Convolutional Neural Networks. Mechanical Systems and Signal Processing. 2020;140. https://doi.org/10.1016/j.ymssp.2020.106683
- 23. Xiong J, Li C, Wang C-D, Cen J, Wang Q, Wang S. Application of Convolutional Neural Network and Data Preprocessing by Mutual Dimensionless and Similar Gram Matrix in Fault Diagnosis. IEEE Transactions on Industrial Informatics. 2022;18(2):1061-71. https://doi.org/10.1109/TII.2021.3073755
- 24. Bai Y, Yang J, Wang J, Zhao Y, Li Q. Image representation of vibration signals and its application in intelligent compound fault diagnosis in railway vehicle wheelset-axlebox assemblies. Mechanical Systems and Signal Processing. 2021;152. https://doi.org/10.1016/j.ymssp.2020.107421
- 25. He D, Lao Z, Jin Z, He C, Shan S, Miao J. Train bearing fault diagnosis based on multi-sensor data fusion and dual-scale residual network. Nonlinear Dynamics. 2023;111(16):14901-24. https://doi.org/10.1007/s11071-023-08638-w
- 26. Fan C, Lin Y, Piscitelli MS, Chiosa R, Wang H, Capozzoli A, et al. Leveraging graph convolutional networks for semi-supervised fault diagnosis of HVAC systems in data-scarce contexts. Building Simulation. 2023;16(8):1499-517. https://doi.org/10.1007/s1227302310411
- 27. Chang C, Wang Q, Jiang J, Jiang Y, Wu T. Voltage fault diagnosis of a power battery based on wavelet time-frequency diagram. Energy. 2023;278. https://doi.org/10.1016/j.energy.2023.127920
- 28. Wang, X., Yao, Y., and Gao, C. (2024). Wasserstein Distance- EEMD Enhanced Multi-Head Graph Attention Network for Rolling Bearing Fault Diagnosis Under Different Working Conditions. Eksploatacja i Niezawodnosc – Maintenance and Reliability. https://doi.org/10.17531/ein/184037
- 29. Tong J, Liu C, Zheng J, Pan H. Multi-sensor information fusion and coordinate attention-based fault diagnosis method and its interpretability research. Engineering Applications of Artificial Intelligence. 2023;124. https://doi.org/10.1016/j.engappai.2023.106614
- 30. Xia S, Zhou X, Shi H, Li S, Xu C. A fault diagnosis method with multi-source data fusion based on hierarchical attention for AUV. Ocean Engineering. 2022;266. https://doi.org/10.1016/j.oceaneng.2022.112595
- 31. Wang Z, Oates T. Imaging time-series to improve classification and imputation. arXiv preprint arXiv:150600327. 2015. https://doi.org/10.48550/arXiv.1506.00327
- 32. Keogh EJ, Pazzani MJ, editors. Scaling up dynamic time warping for datamining applications. Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining; 2000. https://doi.org/10.1145/347090.347153
- 33. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. Densely Connected Convolutional Networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)2017. p. 2261-9. https://doi.org/10.1109/CVPR.2017.243
- 34. Hu J, Shen L, Sun G. Squeeze-and-Excitation Networks. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition2018. p. 7132-41. https://doi.org/10.1109/CVPR.2018.00745
- 35. Smith WA, Randall RB. Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study. Mechanical systems and signal processing. 2015;64:100-31. https://doi.org/10.1016/J.YMSSP.2015.04.021
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-43aa9a6d-37bb-42dc-a83a-f5af8fa3abf7
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.