A novel, machine learning-based feature extraction method for detecting and localizing bearing component defects

• Autorzy: Cherif Bilal Djamal Eddine , Seninete Sara , Defdaf Mabrouk

Identyfikatory

DOI

10.24425/mms.2022.140038

• Języki publikacji: EN

Abstrakty

EN

Vibration analysis for conditional preventive maintenance is an essential tool for the industry. The vibration signals sensored, collected and analyzed can provide information about the state of an induction motor. Appropriate processing of these vibratory signals leads to define a normal or abnormal state of the whole rotating machinery, or in particular, one of its components. The main objective of this paper is to propose a method for automatic monitoring of bearing components condition of an induction motor. The proposed method is based on two approaches with one based on signal processing using the Hilbert spectral envelope and the other approach uses machine learning based on random forests. The Hilbert spectral envelope allows the extraction of frequency characteristics that are considered as new features entering the classifier. The frequencies chosen as features are determined from a proportional variation of their amplitudes with the variation of the load torque and the fault diameter. Furthermore, a random forest-based classifier can validate the effectiveness of extracted frequency characteristics as novel features to deal with bearing fault detection while automatically locating the faulty component with a classification rate of 99.94%. The results obtained with the proposed method have been validated experimentally using a test rig.

Słowa kluczowe

EN

induction motor; bearing; fault; outer race; inner race; ENV; RF

• Wydawca: Komitet Metrologii i Aparatury Naukowej PAN
• Czasopismo: Metrology and Measurement Systems
• Rocznik: 2022
• Tom: Vol. 29, nr 2
• Strony: 333--346
• Opis fizyczny: Bibliogr. 28 poz., rys., tab., wykr., wzory

Twórcy

autor

Cherif Bilal Djamal Eddine

• Department of Electrical Engineering, Faculty of Technology, University of M’sila, M’sila 28000, Algeria

autor

Seninete Sara

• Department of Electrical Engineering, Faculty of Technology, University of Mostaganem, Mostaganem 27000, Algeria

autor

Defdaf Mabrouk

• Department of Electrical Engineering, Faculty of Technology, University of Mostaganem, Mostaganem 27000, Algeria

Bibliografia

• [1] Abdelkader, R., Kaddour, A., Bendiabdellah, A., & Derouiche, Z. (2018). Rolling bearing fault diagnosis based on an improved denoising method using the complete ensemble empirical mode decomposition and the optimized thresholding operation. IEEE Sensors Journal. 18(17), 7166-7172. https://doi.org/10.1109/JSEN.2018.2853136
• [2] Chouidira, I., Khodja, D., & Chakroune, S. (2019). Continuous Wavelet Technique for Detection of Broken Bar Faults in Induction Machine. Traitement du Signal, 36(2), 171-176. https://doi.org/10.18280/ts.360207. (in French)
• [3] Abdelkader, R., Derouiche, Z., Kaddour, A., & Zergoug, M. (2016, November). Rolling bearing faults diagnosis based on empirical mode decomposition: Optimized threshold de-noising method. In 2016 8th International Conference on Modelling, Identification and Control (ICMIC) (pp. 186-191). IEEE. https://doi.org/10.1109/ICMIC.2016.7804296
• [4] Brusa, H., Bruzzone, K, Delprete, C., Di Maggio, L. G., & Rosso, C. (2020). Health indicators construction for damage level assessment in bearing diagnostics: A proposal of an energetic approach based on envelope analysis. Applied Sciences, 10(22), 8131. https://doi.org/10.3390/app10228131
• [5] Eddine, R. C., & Slimane, B. (2020). Detection of bearing defects using Hilbert envelope analysis and fast kurtogram demodulation method. Journal of Electrical Systems, 16(1).
• [6] Darji, A. A., Darji, P. H., & Pandya, D. H. (2020). Envelope Spectrum Analysis with Modified EMD for Fault Diagnosis of Rolling Element Bearing. In: Gupta, V., Varde, P., Kankar, P., & Joshi, N. (Eds.), Reliability and Risk Assessment in Engineering. Lecture Notes in Mechanical Engineering (pp. 91-99). Springer, https://doi.org/10.1007/978-981-15-3746-2_8
• [7] Choudhary, A., Goyal, D., & Letha, S. S. (2020). Infrared thermography-based fault diagnosis of induction motor bearings using machine learning. IEEE Sensors Journal. 21(2), 1727-1734. https://doi.org/10.1109/JSEN.2020.3015868
• [8] Goyal, D., Dhami. S. S., & Pabla, B. S. (2020). Non-contact fault diagnosis of bearings in machine learning environment. IEEE Sensors Journal. 20(9), 4816-4823. https://doi.org/10.1109/JSEN.2020.2964633
• [9] Roy, S. S., Dey, S., & Chatterjee, S. (2020). Autocorrelation aided random forest classifier-based bearing fault detection framework. IEEE Sensors Journal, 20(18), 10792-10800. https://doi.org/10.1109/JSEN.2020.2995109
• [10] Tian, J., Liu, L., Zhang, F., Ai, Y., Wang, R., & Fei, C. (2020). Multi-domain entropy-random forest method for the fusion diagnosis of inter-shaft bearing faults with acoustic emission signals. Entropy, 22(1), 57. https://doi.org/10.3390/e22010057
• [11] Ewert, P., Orlowska-Kowalska, T., & Jankowska, K. (2021). Effectiveness Analysis of PMSM Motor Rolling Bearing Fault Detectors Based on Vibration Analysis and Shallow Neural Networks. Energies, 14(3), 712. https://www.mdpi.com/1996-1073/14/3/712
• [12] Saucedo-Dorantes, J. J., Zamudio-Ramirez, I., Cureno-Osornio, J., Osornio-Rios, R. A., & Antonino-Daviu, J. A. (2021). Condition Monitoring Method for the Detection of Fault Graduality in Outer Race Bearing Based on Vibration-Current Fusion, Statistical Features and Neural Network. Applied Sciences, 11(17), 8033. https://doi.org/10.3390/app11178033
• [13] Kamat, P., Marni, P., Cardoz, L., Irani, A., Gajula, A., Saha, A., Kumar, S., & Sugandhi, R. (2021). Bearing Fault Detection Using Comparative Analysis of Random Forest, ANN, and Autoencoder Methods. In Communication and Intelligent Systems (pp. 157-171). Springer, Singapore. https://doi.org/10.1007/978-981-16-1089-9_14
• [14] Li, H., Liu, T., Wu, X., & Chen. Q. (2019). Application of EEMD and improved frequency band entropy in bearing fault feature extraction. ISA Transactions, 88, 170-185. https://doi.org/10.1016/j.isatra.2018.12.002
• [15] Malla, C., & Panigrahi, I. (2019). Review of condition monitoring of rolling element bearing using vibration analysis and other techniques. Journal of Vibration Engineering & Technologies, 7(4), 407-414. https://doi.org/10.1007/s42417-019-00119-y
• [16] Bearing Data Center - Case Western Reserve university http://csegroups.case.edu/bearingdatacenter/pages/welcome-case-western-reserve-university
• [17] Hoseinzadeh, M. S., Khadem, S. E., & Sadooghi, M. S.(2019) Modyfing the Hilbert-Huang transform using the nonlinear entropy-based features for early fault detection of ball bearings. Applied Acoustics, 150, 313-324. https://doi.org/10.1016/j.apacoust.2019.02.011
• [18] Marques, J. A. L., Cortez, P. C., Madeira, J. P. D. V., Fong, S. J., Schlindwein, F. S., & De Albuquerque. V. H. C. (2019). Automatic cardiotocography diagnostic system based on Hilbert transform and adaptive threshold technique. IEEE Access, 7, 73085-73094. https://doi.org/10.1109/ACCESS.2018.2877933
• [19] Guo, M. F., Yang, N. C., & Chen, W. F. (2019). Deep-learning-based fault classification using Hilbert-Huang transform and convolutional neural network in power distribution systems. IEEE Sensors Journal, 19(16), 6905-6913. https://doi.org/10.1109/JSEN.2019.2913006
• [20] Cherif, B. D. E., Bendiabdellah, A., & Tabbakh, M. (2020). An Automatic Diagnosis of an Inverter IGBT Open-Circuit Fault Based on HHT-ANN. Electric Power Components and Systems, 48(6-7), 589-602. https://doi.org/10.1080/15325008.2020.1793835
• [21] Liu, P., Zhang, Y., Wu, H., & Fu, T. (2020). Optimization of Edge-PLC-Based Fault Diagnosis with Random Forest in Industrial Internet of Things. IEEE Internet of Things Journal, 7(10), 9664-9674. https://doi.org/10.1109/JIOT.2020.2994200
• [22] Patgiri R., Katari H., Kumar R., Sharma D. (2019, January) Empirical Study on Malicious URL Detection Using Machine Learning. In: Fahrnberger G., Gopinathan S., & Parida L. (Eds.). Distributed Computing and Internet Technology. ICDCIT 2019. Lecture Notes in Computer Science, vol. 11319. Springer, Cham, https://doi.org/10.1007/978-3-030-05366-6_31
• [23] Nishat Toma, R., & Kim, J. M. (2020). Bearing fault classification of induction motors using discrete wavelet transform and ensemble machine learning algorithms. Applied Sciences, 10(15). 5251. https://doi.org/10.3390/app10155251
• [24] Costache, R., Pham, Q. B., Sharifi, E., Linh, N. T. T., Abba, S. I., Vojtek, M., Vojteková, J., Nhi, P. T. T., & Khoi, D. N. (2020). Flash-flood susceptibility assessment using multi-criteria decision making and machine learning supported by remote sensing and GIS techniques. Remote Sensing, 12(1), 106. https://doi.org/10.3390/rs12010106
• [25] Saucedo-Dorantes, J. J., Zamudio-Ramirez, I., Cureno-Osornio, J., Osornio-Rios, R. A., & Antonino-Daviu, J. A. (2021). Condition Monitoring Method for the Detection of Fault Gradually in Outer Race Bearing Based on Vibration-Current Fusion, Statistical Features and Neural Network. Applied Sciences, 11(17), 8033. https://doi.org/10.3390/app11178033
• [26] Zhou, H., Cheng, L., Teng, L., & Sun, H. (2021, May). Bearing Fault Diagnosis Based on RF-PCA-LSTM Model. In 2021 2nd Information Communication Technologies Conference (ICTC) (pp. 278-282). IEEE. https://doi.org/10.1109/ICTC51749.2021.9441578
• [27] Qin, X., Xu, D., Dong, X., Cui, X., & Zhang, S. (2021). The Fault Diagnosis of Rolling Bearing Based on Improved Deep Forest. Shock and Vibration. https://doi.org/10.1155/2021/9933137
• [28] Toma, R. N., Prosvirin, A. E., & Kim, J. M. (2020). Bearing fault diagnosis of induction motors using a genetic algorithm and machine learning classifiers. Sensors, 20(7), 1884. https://doi.org/10.3390/s20071884

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).