Application of neural networks in diagnosing engine faults based on vibration signals
33 viewsDOI:
https://doi.org/10.54939/1859-1043.j.mst.94.2024.22-30Keywords:
Engine error; Yolo; Resnet; SVM; Short-Time Fourier Transform STFT.Abstract
This paper investigates and applies artificial intelligence (AI) to improve the monitoring and diagnosis process of electrical engine faults based on vibration signals. The research aims to build a model to collect sample data from engines and utilize three different AI networks in this study, including YOLO (You Only Look Once), Resnet (Residual neural network), and SVM (Support Vector Machine). By applying these models to independently identify faults using the common input signal of vibration, particularly focusing on bearing-related faults in engine systems, the paper concentrates on exploring various faults. The experimental results presented in the paper demonstrate the accuracy of using these networks in diagnosing engine faults and provide important insights into the accuracy and practical applicability of AI networks in the field of industrial equipment maintenance and management.
References
[1]. Rai, A., & Upadhyay, S. H., “A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings”, Tribology International, 96, 289-306, (2016). DOI: https://doi.org/10.1016/j.triboint.2015.12.037
[2]. Saufi, S. R., Ahmad, Z. A. B., Leong, M. S., & Lim, M. H., “Challenges and opportunities of deep learning models for machinery fault detection and diagnosis”, A review. Ieee Access, 7, 122644-122662, (2019). DOI: https://doi.org/10.1109/ACCESS.2019.2938227
[3]. Zhao, Z., Wu, J., Li, T., Sun, C., Yan, R., & Chen, X., “Challenges and opportunities of AI-enabled monitoring, diagnosis & prognosis”, A review. Chinese Journal of Mechanical Engineering, 34(1), 1-29, (2021). DOI: https://doi.org/10.1186/s10033-021-00570-7
[4]. Tahir, M. M., Khan, A. Q., Iqbal, N., Hussain, A., & Badshah, S., “Enhancing fault classification accuracy of ball bearing using central tendency based time domain features”, IEEE Access, 5, 72-83, (2016). DOI: https://doi.org/10.1109/ACCESS.2016.2608505
[5]. Chen, Z.; Li, C.; Sanchez, R.V, “Gearbox fault identification and classification with convolutional neural networks”, Shock. Vib. 2015, 390134, (2015). DOI: https://doi.org/10.1155/2015/390134
[6]. Zhao, J.; Yang, S.; Li, Q.; Liu, Y.; Gu, X.; Liu, W, “A new bearing fault diagnosis method based on signal-to-image mapping and convolutional neural network”, Measurement 2021, 176, 109088, (2021). DOI: https://doi.org/10.1016/j.measurement.2021.109088
[7]. Gao, Y.; Liu, X.; Huang, H.; Xiang, J, “A hybrid of FEM simulations and generative adversarial networks to classify faults in rotor-bearing systems”, ISA Trans. 2021, 108, 356–366, (2021). DOI: https://doi.org/10.1016/j.isatra.2020.08.012
[8]. Liu, H.; Zhou, J.; Xu, Y.; Zheng, Y.; Peng, X.; Jiang, W, “Unsupervised fault diagnosis of rolling bearings using a deep neural network based on generative adversarial networks”, Neurocomputing 2018, 315, 412–424, (2018). DOI: https://doi.org/10.1016/j.neucom.2018.07.034
[9]. Wang, R.; Jiang, H.; Li, X.; Liu, S, “A reinforcement neural architecture search method for rolling bearing fault diagnosis”, Measurement 2020, 154, 107417, (2020). DOI: https://doi.org/10.1016/j.measurement.2019.107417
[10]. Y. A. Almatheel and M. Osman, "Bearing Element Fault Diagnosis Using Support Vector Machine" , 2020 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE), Khartoum, Sudan, pp. 1-5, (2021). DOI: https://doi.org/10.1109/ICCCEEE49695.2021.9429590
[11]. X. Zhang, Y. Liang, J. Zhou, and Y. zang, “A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM,” Measurement, vol. 69, pp. 164–179, (2015). DOI: https://doi.org/10.1016/j.measurement.2015.03.017
[12]. C. Zhang, J. Chen, and X. Guo, “A gear fault diagnosis method based on EMD energy entropy and SVM,” Journal of Vibration and Shock, vol. 29, no. 10, pp. 216–220, (2010).
[13]. S. Zgarni and A. Braham, "Classification of Bearing Fault Detection Using Multiclass SVM: A Comparative Study", 2018 15th International Multi-Conference on Systems, Signals & Devices (SSD), Yasmine Hammamet, Tunisia, pp. 888-892, (2018). DOI: https://doi.org/10.1109/SSD.2018.8570564
[14]. J. Redmon and A. Farhadi, "YOLO9000: better, faster, stronger", in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 7263-7271, (2017). DOI: https://doi.org/10.1109/CVPR.2017.690
[15]. C.-Y. Wang, A. Bochkovskiy, and H.-Y. M. Liao, "YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors", arXiv preprint arXiv:2207.02696, (2022). DOI: https://doi.org/10.1109/CVPR52729.2023.00721
[16]. Van-Nam Pham, Quang-Huy Do Ba, Duc-Anh Tran Le, “Using Artificial Intelligence (AI) for Monitoring and Diagnosing Electric Motor Faults Based on Vibration Signals”, International Conference on Information Networking (ICOIN), (2024).
