Enhancing Bearing Fault Diagnosis in Induction Motors: A Novel Approach Leveraging Synchronized Deviation of Instantaneous Frequency of Voltage and Current

Document Type : Research Article

Authors

Department of Electrical Engineering, University of Kurdistan, Sanandaj, Iran

Abstract

Current-based methods for bearing fault diagnosis primarily rely on analyzing the current signal, leading to challenges in detecting fault frequencies due to their low magnitude amid the noise in the current spectrum. This issue intensifies for weak bearing faults in their early stages. The presence of noise components increases the risk of false alarms, as fault characteristics are often obscured in the raw current spectral analysis. To address this, effective bearing fault diagnosis necessitates the reduction of noise components. This paper presents a novel noise cancellation method that enhances the estimation of bearing fault signals in induction motors by utilizing the deviation of instantaneous frequency in synchronized motor voltage and current signals. The proposed method efficiently diagnoses bearing fault characteristic frequencies during spectral analysis. Simulation and experimental results substantiate the effectiveness of this approach in detecting outer/inner raceway and ball-bearing faults.

Keywords

Main Subjects

References

[1] S. Nazari, S. Shokoohi, J. Moshtagh, A Current Noise Cancellation Method Based on Integration of Stator Synchronized Currents for Bearing Fault Diagnosis, IEEE Transactions on Instrumentation and Measurement,  (2023) 1-1.
[2] F. Dalvand, S. Dalvand, F. Sharafi, M. Pecht, Current noise cancellation for bearing fault diagnosis using time shifting, IEEE Transactions on Industrial Electronics, 64(10) (2017) 8138-8147.
[3] K. Xu, X. Song, A Current Noise Cancellation Method Based on Fractional Linear Prediction for Bearing Fault Detection, Sensors, 24(1) (2023) 52.
[4] B. Guan, X. Bao, H. Qiu, D. Yang, Enhancing bearing fault diagnosis using motor current signals: A novel approach combining time shifting and CausalConvNets, Measurement, 226 (2024) 114049.
[5] M. Sun, H. Wang, P. Liu, Z. Long, J. Yang, S. Huang, A novel data-driven mechanical fault diagnosis method for induction motors using stator current signals, IEEE Transactions on Transportation Electrification, 9(1) (2022) 347-358.
[6] W. Zhou, T.G. Habetler, R.G. Harley, Bearing Fault Detection Via Stator Current Noise Cancellation and Statistical Control, IEEE Transactions on Industrial Electronics, 55(12) (2008) 4260-4269.
[7] R.R. Schoen, T.G. Habetler, F. Kamran, R.G. Bartfield, Motor bearing damage detection using stator current monitoring, IEEE Transactions on Industry Applications, 31(6) (1995) 1274-1279.
[8] M. Arkan, H. Çaliş, M. Tağluk, Bearing and misalignment fault detection in induction motors by using the space vector angular fluctuation signal, Electrical Engineering, 87(4) (2005) 197-206.
[9] A. Bouras, S. Bennedjai, S. Bouras, Experimental detection of defects in variable speed fan bearing using stator current monitoring, SN Applied Sciences, 2 (2020) 1-8.
[10] F.B. Abid, S. Zgarni, A. Braham, Distinct bearing faults detection in induction motor by a hybrid optimized SWPT and aiNet-DAG SVM, IEEE Transactions on Energy Conversion, 33(4) (2018) 1692-1699.
[11] S. Singh, N. Kumar, Detection of bearing faults in mechanical systems using stator current monitoring, IEEE Transactions on Industrial Informatics, 13(3) (2016) 1341-1349.
[12] V.C. Leite, J.G.B. da Silva, G.F.C. Veloso, L.E.B. da Silva, G. Lambert-Torres, E.L. Bonaldi, L.E.d.L. de Oliveira, Detection of localized bearing faults in induction machines by spectral kurtosis and envelope analysis of stator current, IEEE Transactions on Industrial Electronics, 62(3) (2014) 1855-1865.
[13] F. Immovilli, M. Cocconcelli, A. Bellini, R. Rubini, Detection of generalized-roughness bearing fault by spectral-kurtosis energy of vibration or current signals, IEEE Transactions on Industrial Electronics, 56(11) (2009) 4710-4717.
[14] X. Zhang, Y. Liang, J. Zhou, A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM, Measurement, 69 (2015) 164-179.
[15] W. Zhou, B. Lu, T.G. Habetler, R.G. Harley, Incipient bearing fault detection via motor stator current noise cancellation using wiener filter, IEEE Transactions on Industry Applications, 45(4) (2009) 1309-1317.
[16] M.E.H. Benbouzid, M. Vieira, C. Theys, Induction motors' faults detection and localization using stator current advanced signal processing techniques, IEEE Transactions on power electronics, 14(1) (1999) 14-22.
[17] M. Pineda-Sanchez, R. Puche-Panadero, M. Riera-Guasp, J. Perez-Cruz, J. Roger-Folch, J. Pons-Llinares, V. Climente-Alarcon, J.A. Antonino-Daviu, Application of the Teager–Kaiser energy operator to the fault diagnosis of induction motors, IEEE Transactions on energy conversion, 28(4) (2013) 1036-1044.
[18] F. Dalvand, M. Kang, S. Dalvand, M. Pecht, Detection of Generalized-Roughness and Single-Point Bearing Faults Using Linear Prediction-Based Current Noise Cancellation, IEEE Transactions on Industrial Electronics, 65(12) (2018) 9728-9738.
[19] X. Wang, Z. Liu, M. Dai, W. Li, J. Tang, Time-shift denoising Combined with DWT-Enhanced Condition Domain Adaptation for Motor Bearing Fault Diagnosis via Current Signals, IEEE Sensors Journal,  (2024).
[20] X. Wang, R. Meng, G. Wang, X. Liu, X. Liu, D. Lu, The research on fault diagnosis of rolling bearing based on current signal CNN-SVM, Measurement Science and Technology, 34(12) (2023) 125021.
[21] S. Shokoohi, J. Moshtagh, Beyond Signal Processing: A Model-Based Luenberger Observer Approach for Accurate Bearing Fault Diagnosis, AUT Journal of Electrical Engineering,  (2024) -.
[22] A. Ibrahim, M. El Badaoui, F. Guillet, F. Bonnardot, A new bearing fault detection method in induction machines based on instantaneous power factor, IEEE Transactions on Industrial Electronics, 55(12) (2008) 4252-4259.
[23] F. Dalvand, A. Kalantar, M.S. Safizadeh, A Novel Bearing Condition Monitoring Method in Induction Motors Based on Instantaneous Frequency of Motor Voltage, IEEE Transactions on Industrial Electronics, 63(1) (2016) 364-376.
[24] F. Dalvand, A. Kalantar, S. Shokoohi, H. Bevrani, Time-domain bearing condition monitoring in induction motors using instantaneous frequency of motor voltage, in:  2014 Smart Grid Conference (SGC), 2014, pp. 1-7.
[25] J.R. Stack, T.G. Habetler, R.G. Harley, Fault classification and fault signature production for rolling element bearings in electric machines, in:  4th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, 2003. SDEMPED 2003., 2003, pp. 172-176.
AUT Journal of Electrical Engineering
Volume 57, Issue 1
2025
Pages 221-238

Files

Share

How to cite

Statistics