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Motor Bearing Fault Diagnosis Based on Vibration Signal, Wavelet Denoising, and CNN | ||
| AUT Journal of Electrical Engineering | ||
| مقاله 9، دوره 57، شماره 3، 2025، صفحه 551-568 اصل مقاله (1.4 M) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/eej.2025.24182.5658 | ||
| نویسندگان | ||
| Riyadh Abduljaleel mhalhal1؛ Heydar Toossian Shandiz* 1؛ Naser Pariz1؛ Alaa Abdulhady Jaber2 | ||
| 1Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran | ||
| 2Faculty of Engineering, University of Technology, Baghdad, Iraq | ||
| چکیده | ||
| Induction motors are broadly applied in different industries because of their low cost, high efficiency, and reliability. Although failures in such motors could cause significant issues like decreased efficiency, unexpected shutdown, and damage to other system sections. Diagnosing bearing failures is critical to reducing maintenance costs and operational failures. Bearing failures are a major cause of machine vibrations. Unfortunately, existing methods are optimized for controlled environments and disregard realistic conditions such as variable load, time-varying rotational speeds, and the non-stationary nature of vibration. This study presents an integration of time analysis and deep learning techniques to diagnose bearing failures under time-varying speeds and varying noise levels. In this study, we present an approach to diagnosing bearing failures employing vibration signals and convolutional neural networks (CNN) with Pre-processing of the vibration signal by using the discrete wavelet transform (DWT) to remove the effect of Variable Frequency Drive (VFD), which causes odd harmonics. The experimental outcomes show that the presented technique surpasses conventional techniques in the two computational efficiency as well as accuracy to diagnose bearing failures. This work paves the way for further research in the field of bearing fault diagnosis and provides a promising solution for real-world applications. | ||
| کلیدواژهها | ||
| Bearing Fault؛ Vibration Signal؛ DWT؛ CNN؛ Time Analysis؛ Induction Motor | ||
| مراجع | ||
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[1] P. Gangsar and R. Tiwari, “Multifault Diagnosis of Induction Motor at Intermediate Operating Conditions Using Wavelet Packet Transform and Support Vector Machine,” Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME, vol. 140, no. 8, Aug. 2018, doi: 10.1115/1.4039204.
[2] A. Boudiaf, A. Moussaoui, A. Dahane, and I. Atoui, “A Comparative Study of Various Methods of Bearing Faults Diagnosis Using the Case Western Reserve University Data,” Journal of Failure Analysis and Prevention, vol. 16, no. 2, pp. 271–284, Apr. 2016, doi: 10.1007/s11668-016-0080-7.
[3] J. Liu, L. Xue, L. Wang, Z. Shi, and M. Xia, “A new impact model for vibration features of a defective ball bearing,” ISA Trans, vol. 142, pp. 465–477, Nov. 2023, doi: 10.1016/j.isatra.2023.08.014.
[4] T. Ali, D. #1, A. Abdulhady, and J. #2, “Bearing Fault Diagnosis Using Motor Current Signature Analysis and the Artificial Neural Network,” vol. 10, no. 1, 2020.
[5] C. Abdelkrim, M. S. Meridjet, N. Boutasseta, and L. Boulanouar, “Detection and classification of bearing faults in industrial geared motors using temporal features and adaptive neuro-fuzzy inference system,” Heliyon, vol. 5, no. 8, Aug. 2019, doi: 10.1016/j.heliyon.2019.e02046.
[6] C. Wang, M. Wang, B. Yang, K. Song, Y. Zhang, and L. Liu, “A novel methodology for fault size estimation of ball bearings using stator current signal,” Measurement (Lond), vol. 171, Feb. 2021, doi: 10.1016/j.measurement.2020.108723.
[7] P. Guo, J. Fu, and X. Yang, “Condition monitoring and fault diagnosis of wind turbine gearbox bearing temperature based on kolmogorov-smirnov test and convolutional neural network model,” Energies (Basel), vol. 11, no. 9, Sep. 2018, doi: 10.3390/en11092248.
[8] H. Zhao, H. Liu, Y. Jin, X. Dang, and W. Deng, “Feature Extraction for Data-Driven Remaining Useful Life Prediction of Rolling Bearings,” IEEE Trans Instrum Meas, vol. 70, 2021, doi: 10.1109/TIM.2021.3059500.
[9] M. Irfan et al., “A Comparison of Machine Learning Methods for the Diagnosis of Motor Faults Using Automated Spectral Feature Extraction Technique,” J Nondestr Eval, vol. 41, no. 2, p. 31, Jun. 2022, doi: 10.1007/s10921-022-00856-3.
[10] H. Shi, Y. Li, X. Bai, K. Zhang, and X. Sun, “A two-stage sound-vibration signal fusion method for weak fault detection in rolling bearing systems,” Mech Syst Signal Process, vol. 172, p. 109012, Jun. 2022, doi: 10.1016/j.ymssp.2022.109012.
[11] A. A. Jaber, “Diagnosis of Bearing Faults Using Temporal Vibration Signals: A Comparative Study of Machine Learning Models with Feature Selection Techniques,” Journal of Failure Analysis and Prevention, vol. 24, no. 2, pp. 752–768, Apr. 2024, doi: 10.1007/s11668-024-01883-0.
[12] R. Liu, B. Yang, E. Zio, and X. Chen, “Artificial intelligence for fault diagnosis of rotating machinery: A review,” Mech Syst Signal Process, vol. 108, pp. 33–47, Aug. 2018, doi: 10.1016/j.ymssp.2018.02.016.
[13] V. V. Rao and C. Ratnam, “Estimation of Defect Severity in Rolling Element Bearings using Vibration Signals with Artificial Neural Network,” 2015.
[14] L. A. Al-Haddad and A. A. Jaber, “An Intelligent Fault Diagnosis Approach for Multirotor UAVs Based on Deep Neural Network of Multi-Resolution Transform Features,” Drones, vol. 7, no. 2, Feb. 2023, doi: 10.3390/drones7020082..
[15] T. T. Vo, M. K. Liu, and M. Q. Tran, “Harnessing attention mechanisms in a comprehensive deep learning approach for induction motor fault diagnosis using raw electrical signals,” Eng Appl Artif Intell, vol. 129, Mar. 2024, doi: 10.1016/j.engappai.2023.107643.
[16] S. Agrawal and V. K. Giri, “Improved mechanical fault identification of an induction motor using Teager-Kaiser energy operator,” Journal of Electrical Engineering and Technology, vol. 12, no. 5, pp. 1955–1962, Sep. 2017, doi: 10.5370/JEET.2017.12.5.1955.
[17] M. Savolainen and A. Lehtovaara, “Development of damage detection parameters over the lifetime of a rolling element bearing,” Tribologia - Finnish Journal of Tribology, vol. 38, no. 3−4, Dec. 2021, doi: 10.30678/fjt.112092.
[18] H. Shi, Z. Liu, X. Bai, Y. Li, and Y. Wu, “A theoretical model with the effect of cracks in the local spalling of full ceramic ball bearings,” Applied Sciences (Switzerland), vol. 9, no. 19, Oct. 2019, doi: 10.3390/app9194142.
[19] P. Wang et al., “Vibration characteristics of rotor-bearing system with angular misalignment and cage fracture: Simulation and experiment,” Mech Syst Signal Process, vol. 182, p. 109545, Jan. 2023, doi: 10.1016/j.ymssp.2022.109545.
[20] C.-Y. Lee and W.-C. Lin, “Induction Motor Fault Classification Based on ROC Curve and t-SNE,” IEEE Access, vol. 9, pp. 56330–56343, 2021, doi: 10.1109/ACCESS.2021.3072646.
[21] X. Liu, H. Huang, and J. Xiang, “A personalized diagnosis method to detect faults in a bearing based on acceleration sensors and an fem simulation driving support vector machine,” Sensors (Switzerland), vol. 20, no. 2, Jan. 2020, doi: 10.3390/s20020420.
[22] H. Liu, L. Li, and J. Ma, “Rolling Bearing Fault Diagnosis Based on STFT-Deep Learning and Sound Signals,” Shock and Vibration, vol. 2016, 2016, doi: 10.1155/2016/6127479.
[23] C. T. Alexakos, Y. L. Karnavas, M. Drakaki, and I. A. Tziafettas, “A Combined Short Time Fourier Transform and Image Classification Transformer Model for Rolling Element Bearings Fault Diagnosis in Electric Motors,” Mach Learn Knowl Extr, vol. 3, no. 1, pp. 228–242, Mar. 2021, doi: 10.3390/make3010011.
[24] A. B. Patil, J. A. Gaikwad, and J. V. Kulkarni, “Bearing fault diagnosis using discrete Wavelet Transform and Artificial Neural Network,” in 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), IEEE, 2016, pp. 399–405. doi: 10.1109/ICATCCT.2016.7912031.
[25] F. He and Q. Ye, “A Bearing Fault Diagnosis Method Based on Wavelet Packet Transform and Convolutional Neural Network Optimized by Simulated Annealing Algorithm,” Sensors, vol. 22, no. 4, Feb. 2022, doi: 10.3390/s22041410.
[26] C. Grover and N. Turk, “Rolling Element Bearing Fault Diagnosis using Empirical Mode Decomposition and Hjorth Parameters,” in Procedia Computer Science, Elsevier B.V., 2020, pp. 1484–1494. doi: 10.1016/j.procs.2020.03.359.
[27] Y. Hou et al., “Acoustic feature enhancement in rolling bearing fault diagnosis using sparsity-oriented multipoint optimal minimum entropy deconvolution adjusted method,” Applied Acoustics, vol. 201, p. 109105, Dec. 2022, doi: 10.1016/j.apacoust.2022.109105.
[28] M. Altaf, T. Akram, M. A. Khan, M. Iqbal, M. M. I. Ch, and C. H. Hsu, “A New Statistical Features Based Approach for Bearing Fault Diagnosis Using Vibration Signals,” Sensors, vol. 22, no. 5, Mar. 2022, doi: 10.3390/s22052012.
[29] S. Tang, S. Yuan, and Y. Zhu, “Deep learning-based intelligent fault diagnosis methods toward rotating machinery,” IEEE Access, vol. 8, pp. 9335–9346, 2020, doi: 10.1109/ACCESS.2019.2963092.
[30] B. Song, Y. Liu, J. Fang, W. Liu, M. Zhong, and X. Liu, “An optimized CNN-BiLSTM network for bearing fault diagnosis under multiple working conditions with limited training samples,” Neurocomputing, vol. 574, Mar. 2024, doi: 10.1016/j.neucom.2024.127284.
[31] J. Zarei and J. Poshtan, “Bearing fault detection using wavelet packet transform of induction motor stator current,” Tribol Int, vol. 40, no. 5, pp. 763–769, May 2007, doi: 10.1016/j.triboint.2006.07.002.
[32] A. Ibrahim, M. El Badaoui, F. Guillet, and F. Bonnardot, “A new bearing fault detection method in induction machines based on instantaneous power factor,” IEEE Transactions on Industrial Electronics, vol. 55, no. 12, pp. 4252–4259, 2008, doi: 10.1109/TIE.2008.2003211.
[33] Proceedings, IECON 2012 : 38th Annual Conference of the IEEE Industrial Electronics Society : École de Technologie Supérieure de Montréal, Université du Québec, Montreal, Canada, 25-28 October, 2012. IEEE, 2012.
[34] G. Niu, X. Dong, and Y. Chen, “Motor Fault Diagnostics Based on Current Signatures: A Review,” 2023, Institute of Electrical and Electronics Engineers Inc. doi: 10.1109/TIM.2023.3285999.
[35] A. Ibrahim’, M. El Badaoui’, F. Guillet’, and M. Zoaeter2, “Using the Cyclostationarity of Electrical Signal for Bearing Fault Detection in Induction Machine.”
[36] M. Huang, Z. Liu, and Y. Tao, “Mechanical fault diagnosis and prediction in IoT based on multi-source sensing data fusion,” Simul Model Pract Theory, vol. 102, Jul. 2020, doi: 10.1016/j.simpat.2019.101981.
[37] D. Cascales-Fulgencio, E. Quiles-Cucarella, and E. García-Moreno, “Computation and Statistical Analysis of Bearings’ Time- and Frequency-Domain Features Enhanced Using Cepstrum Pre-Whitening: A ML- and DL-Based Classification,” Applied Sciences (Switzerland), vol. 12, no. 21, Nov. 2022, doi: 10.3390/app122110882.
[38] M. A. Jamil, M. A. A. Khan, and S. Khanam, “Feature-based performance of SVM and KNN classifiers for diagnosis of rolling element bearing faults,” in Vibroengineering Procedia, EXTRICA, Dec. 2021, pp. 36–42. doi: 10.21595/vp.. 2021.22307.
[39] M. Alonso-Gonzalez, V. G. Diaz, B. Lopez Perez, B. Cristina Pelayo G-Bustelo, and J. P. Anzola, “Bearing Fault Diagnosis With Envelope Analysis and Machine Learning Approaches Using CWRU Dataset,” IEEE Access, vol. 11, pp. 57796–57805, 2023, doi: 10.1109/ACCESS.2023.3283466.
[40] G. S., M. G.E., and S. M., “Fault Prediction Using Fuzzy Convolution Neural Network on IOT Environment with Heterogeneous Sensing Data Fusion,” Bonfring International Journal of Networking Technologies and Applications, vol. 11, no. 1, pp. 01–05, Jan. 2024, doi: 10.9756/bijnta/v11i1/bij24001. | ||
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