Detection of bearing faults based on band-pass filters and Fourier interpolation of the load torque
Email:
Từ khóa:
Bearing faults, band-pass filter, Fourier interpolation
Tóm tắt
Bearing faults are widely found in mechatronics systems especially those that are required to work with unbalance loads. A highly reliable mechatronics system requires high quality bearings and/or effective bearing fault diagnostic procedure. This paper deals with the detection of rolling bearing faults based on band-pass filters and Fourier interpolation of the load torque. The reference torque, which is the output of the speed controller, is considered to be an approximation of the load torque. The reference torque is band-pass filtered and then interpolated in terms of Fourier series by using a sliding window method. The Fourier coefficients associated with a healthy bearing are served as a baseline and are compared with faulty lines corresponding to faulty bearings to detect the presence of a bearing failure. The proposed diagnostic method can be conducted online and does not require any additional sensors. Furthermore, the proposed method is able to detect single-point defects whose faulty levels are located at level C of the ISO 10816 Part 3. A mechatronic system equipped with artificial bearing faults is built in the laboratory to verify the effectiveness of the proposed methodTài liệu tham khảo
[1]. W. F. Braun, B. G. Douglass, C. R. Heising, D. O. Koval, P. O’Donnell, EEE Recommended Practice for the Design of Reliable Industrial and Commercial Power Systems (Gold Book), IEEE Std 493-1997 IEEE Gold Book, (1998) 1–464. https://doi.org/10.1109/IEEESTD.1998.89291
[2]. X. Dai, Z. Gao, From Model, Signal to Knowledge: A Data-Driven Perspective of Fault Detection and Diagnosis, IEEE Trans. Ind. Inform., 9 (2013) 2226–2238. https://doi.org/10.1109/TII.2013.2243743
[3]. S. Zhang, Model-Based Analysis and Quantification of Bearing Faults in Induction Machines, IEEE Trans. Ind. Appl., 56 (2020) 2158–2170. https://doi.org/10.1109/TIA.2020.2979383
[4]. M. Ojaghi, M. Sabouri, J. Faiz, Analytic Model for Induction Motors Under Localized Bearing Faults, IEEE Trans. Energy Convers., 33 (2018) 617–626. https://doi.org/10.1109/TEC.2017.2758382
[5]. D. T. Hoang, H. J. Kang, A Motor Current Signal-Based Bearing Fault Diagnosis Using Deep Learning and Information Fusion, IEEE Trans. Instrum. Meas., 69 (2020) 3325–3333. https://doi.org/10.1109/TIM.2019.2933119
[6]. F. Dalvand, S. Dalvand, F. Sharafi, M. Pecht, Current Noise Cancellation for Bearing Fault Diagnosis Using Time Shifting, IEEE Trans. Ind. Electron., 64 (2017) 8138–8147. https://doi.org/10.1109/TIE.2017.2694397
[7]. E. Elbouchikhi, V. Choqueuse, F. Auger, M. E. H. Benbouzid, Motor Current Signal Analysis Based on a Matched Subspace Detector, IEEE Trans. Instrum. Meas., 66 (2017) 3260–3270. https://doi.org/10.1109/TIM.2017.2749858
[8]. L. Wen, X. Li, L. Gao, Y. Zhang, A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method, IEEE Trans. Ind. Electron., 65 (2018) 5990–5998. https://doi.org/10.1109/TIE.2017.2774777
[9]. P. Cao, S. Zhang, J. Tang, Preprocessing-Free Gear Fault Diagnosis Using Small Datasets With Deep Convolutional Neural Network-Based Transfer Learning, IEEE Access, 6 (2018) 26241–26253. https://doi.org/10.1109/ACCESS.2018.2837621
[10]. T. Han, C. Liu, W. Yang, D. Jiang, Deep Transfer Network with Joint Distribution Adaptation: A New Intelligent Fault Diagnosis Framework for Industry Application, ISA Trans., 97 (2020) 269–281. https://doi.org/10.1016/j.isatra.2019.08.012
[11]. Y. Lu, Z. Song, Q. Gao, D. Zhu, D. Sun, Bearing fault diagnosis based on multi-band filtering, IET Sci. Meas. Technol., 16 (2022) 101–117. https://doi.org/10.1049/smt2.12090
[12]. W. Guo, An Optimal Band-pass Filter based on Adaptive Identification of Bearing Resonant Frequency Band, in 2020 Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling (APARM), (2020) 1–7. https://doi.org/10.1109/APARM49247.2020.9209420
[13]. L. Wang, J. Xiang, A Simulation Based Band-Pass Filter to Improve the Polynomial Chirplet Transform in Fault Detection, in 2018 Prognostics and System Health Management Conference (PHM-Chongqing), (2018) 129–133. https://doi.org/10.1109/PHM-Chongqing.2018.00028
[14]. J. R. Stack, T. G. Habetler, R. G. Harley, Fault classification and fault signature production for rolling element bearings in electric machines, IEEE Trans. Ind. Appl., 40 (2004) 735–739. https://doi.org/10.1109/TIA.2004.827454
[15]. S. A. McInerny, Y. Dai, Basic vibration signal processing for bearing fault detection, IEEE Trans. Educ., 46 (2003) 149–156. https://doi.org/10.1109/TE.2002.808234
[16]. R. Rubini, U. Meneghetti, Application of the envelope and wavelet transform analyses for the diagnosis of incipient faults in ball bearings, Mech. Syst. Signal Process., 15 (2001) 287–302. https://doi.org/10.1006/mssp.2000.1330
[17]. M. E. H. Benbouzid, A review of induction motors signature analysis as a medium for faults detection, in Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Society, 4 (1998) 1950–1955. https://doi.org/10.1109/IECON.1998.724016
[18]. D. Pavkovic, J. Deur, I. Kolmanovsky, Adaptive Kalman Filter-Based Load Torque Compensator for Improved SI Engine Idle Speed Control, IEEE Trans. Control Syst. Technol., 17 (2009) 98–110. https://doi.org/10.1109/TCST.2008.922556
[19]. T. Shi, Z. Wang, C. Xia, Speed Measurement Error Suppression for PMSM Control System Using Self-Adaption Kalman Observer, IEEE Trans. Ind. Electron., 62 (2015) 2753–2763. https://doi.org/10.1109/TIE.2014.2364989
[20]. A. G. Yepes, F. D. Freijedo, J. Doval-Gandoy, Ó. López, J. Malvar, P. Fernandez-Comesaña, Effects of Discretization Methods on the Performance of Resonant Controllers, IEEE Trans. Power Electron., 25 (2010) 1692–1712. https://doi.org/10.1109/TPEL.2010.2041256
[21]. R. Isermann, Identifikation dynamischer Systeme: Band II: Parameterschätzmethoden, Kennwertermittlung und Modellabgleich, Zeitvariante, nichtlineare und Mehrgrößen-Systeme, Anwendungen. Berlin Heidelberg: Springer-Verlag, 1988. Accessed: (2021). [Online]. Available: https://www.springer.com/us/book/9783642970702
[22]. ISO Standard 10816, Mechanical Vibration-Elavuation of Machine Vibration by Measurement on Non-Rotating Parts- Part 3.
[23]. H. Zoubek, S. Villwock, M. Pacas, Frequency Response Analysis for Rolling-Bearing Damage Diagnosis, IEEE Trans. Ind. Electron., 55 (2008) 4270–4276. https://doi.org/10.1109/TIE.2008.2005020
[24]. J. R. Stack, T. G. Habetler, R. G. Harley, Fault-signature modeling and detection of inner-race bearing faults, IEEE Trans. Ind. Appl., 42 (2006) 61–68. https://doi.org/10.1109/TIA.2005.861365
[2]. X. Dai, Z. Gao, From Model, Signal to Knowledge: A Data-Driven Perspective of Fault Detection and Diagnosis, IEEE Trans. Ind. Inform., 9 (2013) 2226–2238. https://doi.org/10.1109/TII.2013.2243743
[3]. S. Zhang, Model-Based Analysis and Quantification of Bearing Faults in Induction Machines, IEEE Trans. Ind. Appl., 56 (2020) 2158–2170. https://doi.org/10.1109/TIA.2020.2979383
[4]. M. Ojaghi, M. Sabouri, J. Faiz, Analytic Model for Induction Motors Under Localized Bearing Faults, IEEE Trans. Energy Convers., 33 (2018) 617–626. https://doi.org/10.1109/TEC.2017.2758382
[5]. D. T. Hoang, H. J. Kang, A Motor Current Signal-Based Bearing Fault Diagnosis Using Deep Learning and Information Fusion, IEEE Trans. Instrum. Meas., 69 (2020) 3325–3333. https://doi.org/10.1109/TIM.2019.2933119
[6]. F. Dalvand, S. Dalvand, F. Sharafi, M. Pecht, Current Noise Cancellation for Bearing Fault Diagnosis Using Time Shifting, IEEE Trans. Ind. Electron., 64 (2017) 8138–8147. https://doi.org/10.1109/TIE.2017.2694397
[7]. E. Elbouchikhi, V. Choqueuse, F. Auger, M. E. H. Benbouzid, Motor Current Signal Analysis Based on a Matched Subspace Detector, IEEE Trans. Instrum. Meas., 66 (2017) 3260–3270. https://doi.org/10.1109/TIM.2017.2749858
[8]. L. Wen, X. Li, L. Gao, Y. Zhang, A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method, IEEE Trans. Ind. Electron., 65 (2018) 5990–5998. https://doi.org/10.1109/TIE.2017.2774777
[9]. P. Cao, S. Zhang, J. Tang, Preprocessing-Free Gear Fault Diagnosis Using Small Datasets With Deep Convolutional Neural Network-Based Transfer Learning, IEEE Access, 6 (2018) 26241–26253. https://doi.org/10.1109/ACCESS.2018.2837621
[10]. T. Han, C. Liu, W. Yang, D. Jiang, Deep Transfer Network with Joint Distribution Adaptation: A New Intelligent Fault Diagnosis Framework for Industry Application, ISA Trans., 97 (2020) 269–281. https://doi.org/10.1016/j.isatra.2019.08.012
[11]. Y. Lu, Z. Song, Q. Gao, D. Zhu, D. Sun, Bearing fault diagnosis based on multi-band filtering, IET Sci. Meas. Technol., 16 (2022) 101–117. https://doi.org/10.1049/smt2.12090
[12]. W. Guo, An Optimal Band-pass Filter based on Adaptive Identification of Bearing Resonant Frequency Band, in 2020 Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling (APARM), (2020) 1–7. https://doi.org/10.1109/APARM49247.2020.9209420
[13]. L. Wang, J. Xiang, A Simulation Based Band-Pass Filter to Improve the Polynomial Chirplet Transform in Fault Detection, in 2018 Prognostics and System Health Management Conference (PHM-Chongqing), (2018) 129–133. https://doi.org/10.1109/PHM-Chongqing.2018.00028
[14]. J. R. Stack, T. G. Habetler, R. G. Harley, Fault classification and fault signature production for rolling element bearings in electric machines, IEEE Trans. Ind. Appl., 40 (2004) 735–739. https://doi.org/10.1109/TIA.2004.827454
[15]. S. A. McInerny, Y. Dai, Basic vibration signal processing for bearing fault detection, IEEE Trans. Educ., 46 (2003) 149–156. https://doi.org/10.1109/TE.2002.808234
[16]. R. Rubini, U. Meneghetti, Application of the envelope and wavelet transform analyses for the diagnosis of incipient faults in ball bearings, Mech. Syst. Signal Process., 15 (2001) 287–302. https://doi.org/10.1006/mssp.2000.1330
[17]. M. E. H. Benbouzid, A review of induction motors signature analysis as a medium for faults detection, in Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Society, 4 (1998) 1950–1955. https://doi.org/10.1109/IECON.1998.724016
[18]. D. Pavkovic, J. Deur, I. Kolmanovsky, Adaptive Kalman Filter-Based Load Torque Compensator for Improved SI Engine Idle Speed Control, IEEE Trans. Control Syst. Technol., 17 (2009) 98–110. https://doi.org/10.1109/TCST.2008.922556
[19]. T. Shi, Z. Wang, C. Xia, Speed Measurement Error Suppression for PMSM Control System Using Self-Adaption Kalman Observer, IEEE Trans. Ind. Electron., 62 (2015) 2753–2763. https://doi.org/10.1109/TIE.2014.2364989
[20]. A. G. Yepes, F. D. Freijedo, J. Doval-Gandoy, Ó. López, J. Malvar, P. Fernandez-Comesaña, Effects of Discretization Methods on the Performance of Resonant Controllers, IEEE Trans. Power Electron., 25 (2010) 1692–1712. https://doi.org/10.1109/TPEL.2010.2041256
[21]. R. Isermann, Identifikation dynamischer Systeme: Band II: Parameterschätzmethoden, Kennwertermittlung und Modellabgleich, Zeitvariante, nichtlineare und Mehrgrößen-Systeme, Anwendungen. Berlin Heidelberg: Springer-Verlag, 1988. Accessed: (2021). [Online]. Available: https://www.springer.com/us/book/9783642970702
[22]. ISO Standard 10816, Mechanical Vibration-Elavuation of Machine Vibration by Measurement on Non-Rotating Parts- Part 3.
[23]. H. Zoubek, S. Villwock, M. Pacas, Frequency Response Analysis for Rolling-Bearing Damage Diagnosis, IEEE Trans. Ind. Electron., 55 (2008) 4270–4276. https://doi.org/10.1109/TIE.2008.2005020
[24]. J. R. Stack, T. G. Habetler, R. G. Harley, Fault-signature modeling and detection of inner-race bearing faults, IEEE Trans. Ind. Appl., 42 (2006) 61–68. https://doi.org/10.1109/TIA.2005.861365
Tải xuống
Chưa có dữ liệu thống kê
Nhận bài
14/07/2023
Nhận bài sửa
21/08/2023
Chấp nhận đăng
23/08/2023
Xuất bản
15/09/2023
Chuyên mục
Công trình khoa học
Kiểu trích dẫn
Van Trang, P., & Thanh Lich, N. (1694710800). Detection of bearing faults based on band-pass filters and Fourier interpolation of the load torque. Tạp Chí Khoa Học Giao Thông Vận Tải, 74(7), 775-789. https://doi.org/10.47869/tcsj.74.7.2
Số lần xem tóm tắt
93
Số lần xem bài báo
107
