Detection of Rotor Unbalance in Multi-Mass Systems Using FFT and Bispectrum
References
- AbdulBary, M., Embaby, A. and Gomaa, F. (2021). Fault Diagnosis in Rotating System Based on Vibration Analysis. Engineering Research Journal (ERJ), 44(3), pp. 285–294. doi: 10.21608/erjm.2021.61436.1080
- Argenti, F., Nesi, P. and Pantaleo, G. (2011). Automatic Transcription of Polyphonic Music Based on the Constant-Q Bispectral Analysis. IEEE Transactions on Audio, Speech, and Language Processing, 19(6), pp. 1610–1630. doi: 10.1109/TASL.2010.2093894
- Boudiaf, M., Cherroun, L. and Benbrika, M. (2018). Real-Time Diagnosis of Three-Phase Induction Machine Using Arduino-Uno Card Based on Park’s Circle Method. Diagnostyka, 19(3), pp. 63–71. doi: 10.29354/diag/90641
- Calderon-Uribe, U., Lizarraga-Morales, R. A. and Guryev, I. V. (2023). Unbalance Detection in Induction Motors Through Vibration Signals Using Texture Features. Applied Sciences, 13(10), p. 6137. doi: 10.3390/app13106137
- Chen, Y., Liang, S., Li, W., Liang, H. and Wang, C. (2019). Faults and Diagnosis Methods of Permanent Magnet Synchronous Motors: A Review. Applied Sciences, 9(10), p. 2116. doi: 10.3390/app9102116
- Diversi, R., Lenzi, A., Speciale, N. and Barbieri, M. (2025). An Autoregressive-Based Motor Current Signature Analysis Approach for Fault Diagnosis of Electric Motor-Driven Mechanisms. Sensors, 25(4), p. 1130. doi: 10.3390/s25041130
- Dogan, Z. and Tetik, K. (2021). Diagnosis of Inter-Turn Faults Based on Fault Harmonic Component Tracking in LSPMSMs Working Under Nonstationary Conditions. IEEE Access, 9, pp. 92101–92112. doi: 10.1109/ACCESS.2021.3092605
- Ebrahimi, B. J., Faiz, J. and Roshtkhari, M. J. (2009). Static-, Dynamic-, and Mixed-Eccentricity Fault Diagnoses in Permanent-Magnet Synchronous Motors. IEEE Transactions on Industrial Electronics, 56(11), pp. 4727–4739. doi: 10.1109/TIE.2009.2029577
- Ewert, P. (2020). The Application of the Bispectrum Analysis to Detect the Rotor Unbalance of the Induction Motor Supplied by the Mains and Frequency Converter. Energies, 13(11), p. 3009. doi: 10.3390/en13113009
- Ewert, P., Kowalski, C. T. and Jaworski, M. (2022). Comparison of the Effectiveness of Selected Vibration Signal Analysis Methods in the Rotor Unbalance Detection of PMSM Drive System. Electronics, 11(11), p. 1748. doi: 10.3390/electronics11111748
- Ewert, P., Kowalski, C. T. and Orlowska-Kowalska, T. (2020). Low-Cost Monitoring and Diagnosis System for Rolling Bearing Faults of the Induction Motor Based on Neural Network Approach. Electronics, 9(9), p. 1334. doi: 10.3390/electronics9091334
- Ewert, P., Pajchrowski, T. and Wicher, B. (2024). Sensorless Detection of Mechanical Unbalance in Servodrive with Elastic Coupling. Energies, 17(19), p. 4859. doi: 10.3390/en17194859
- Faiz, J., Takbash, A. M. and Mazaheri-Tehrani, E. (2017). Application of Signal Processing Tools for Fault Diagnosis in Induction Motors—A Review—Part I. AUT Journal of Electrical Engineering, 49(2), pp. 109–122. doi: 10.22060/eej.2017.13219.5142
- Hachemi Benbouzid, M. E. (2000). A Review of Induction Motors Signature Analysis as a Medium for Faults Detection. IEEE Transactions on Industrial Electronics, 47(5), pp. 984–993. doi: 10.1109/41.873206
- Jia, N., Cheng, Y., Liu, Y. and Tian, Y. (2022). Intelligent Fault Diagnosis of Rotating Machines Based on Wavelet Time–Frequency Diagram and Optimized Stacked Denoising Auto-Encoder. IEEE Sensors Journal, 22(17), pp. 17139–17150. doi: 10.1109/JSEN.2022.3193943
- Kim, H. (2009). On-Line Mechanical Unbalance Estimation for Permanent Magnet Synchronous Machine Drives. IET Electric Power Applications, 3(3), pp. 178–186. doi: 10.1049/iet-epa.2008.0078
- Korkua, S., Jain, H., Lee, W.-J. and Kwan, C. (2010). Wireless health monitoring system for vibration detection of induction motors. In: IEEE Industrial and Commercial Power Systems Technical Conference. doi: 10.1109/ICPS.2010.5489899.
- Krichen, M., Benhadj, N., Chaeib, M. and Neji, R. (2017). Fault detection and diagnosis methods in permanent magnet synchronous machines: A review. In: International Conference on Recent Advances in Electrical Systems, pp. 229–237. Available at:
https://www.researchgate.net/publication/328610743_Fault_Detection_and_Diagnosis_Methods_in_Permanent_Magnet_Synchronous_Machines_A_Review [Accessed 11 May 2026] - Liang, H., Chen, Y., Liang, S. and Wang, C. (2018). Fault Detection of Stator Inter-Turn Short-Circuit in PMSM on Stator Current and Vibration Signal. Applied Sciences, 8(9), p. 1677. doi: 10.3390/app8091677
- Łuczak, D. (2024). Machine Fault Diagnosis Through Vibration Analysis: Continuous Wavelet Transform With Complex Morlet Wavelet and Time–Frequency RGB Image Recognition via Convolutional Neural Network. Electronics, 13(2), p. 452. doi: 10.3390/electronics13020452
- Matsuoka, T. and Ulrych, T. J. (1984). Phase Estimation Using the Bispectrum. Proceedings of the IEEE, 72(10), pp. 1403–1411. doi: 10.1109/PROC.1984.13027
- Nandi, S., Toliyat, H. A. and Li, X. (2005). Condition Monitoring and Fault Diagnosis of Electrical Motors—A Review. IEEE Transactions on Energy Conversion, 20(4), pp. 719–729. doi: 10.1109/TEC.2005.847955
- Niu, G., Dong, X. and Chen, Y. (2023). Motor Fault Diagnostics Based on Current Signatures: A Review. IEEE Transactions on Instrumentation and Measurement, 72, pp. 1–19. doi: 10.1109/TIM.2023.3285999
- Orhan, A., Yordanov, N., Ertarğın, M., Zhilevski, M. and Mikhov, M. (2025). A Comparative Study of Time–Frequency Representations for Bearing and Rotating Fault Diagnosis Using Vision Transformer. Machines, 13(8), p. 737. doi: 10.3390/machines13080737
- Orlowska-Kowalska, T., Wolkiewicz, M., Pietrzak, P., Skowron, M., Ewert, P., Tarchala, G., Krzysztofiak, M. and Kowalski, C. T. (2022). Fault Diagnosis and Fault-Tolerant Control of PMSM Drives—State of the Art and Future Challenges. IEEE Access, 10, pp. 59979–60024. doi: 10.1109/ACCESS.2022.3180153
- Rahman, M. M. and Uddin, M. N. (2017). Online Unbalanced Rotor Fault Detection of an IM Drive Based on Both Time and Frequency Domain Analyses. IEEE Transactions on Industry Applications, 53(4), pp. 4087–4096. doi: 10.1109/TIA.2017.2691736
- Ribeiro Junior, R. F., De Almeida, F. A. and Gomes, G. F. (2020b). Fault Classification in Three-Phase Motors Based on Vibration Signal Analysis and Artificial Neural Networks. Neural Computing and Applications, 32(18), pp. 15171–15189. doi: 10.1007/s00521-020-04868-w
- Ruqiang, Y., Robert, X. G. and Xuefeng, C. (2014). Wavelets for Fault Diagnosis of Rotary Machines: A Review With Applications. Signal Processing, 96(Part A), pp. 1–15. doi: 10.1016/j.sigpro.2013.04.015
- Schmidt, O. T. (2020). Bispectral Mode Decomposition of Nonlinear Flows. Nonlinear Dynamics, 102, pp. 2479–2501. doi: 10.1007/s11071-020-06037-z
- Sergakis, A., Salinas, M., Gkiolekas, N. and Gyftakis, K. N. (2025). A Review of Condition Monitoring of Permanent Magnet Synchronous Machines: Techniques, Challenges and Future Directions. Energies, 18(5), p. 1177. doi: 10.3390/en18051177
- Sun, L., Feng, Z., Lu, N., Wang, B. and Zhang, W. (2019). An Advanced Bispectrum Features for EEG-Based Motor Imagery Classification. Expert Systems with Applications, 131, pp. 9–19. doi: 10.1016/j.eswa.2019.04.021
- Thang, J., Yang, Y., Chen, J., Qiu, R. and Liu, Z. (2020). Characteristics Analysis and Measurement of Inverter-Fed Induction Motors for Stator and Rotor Fault Detection. Energies, 13(1), p. 101. doi: 10.3390/en13010101
- Thomson, W. T. and Fenger, M. (2001). Current Signature Analysis to Detect Induction Motor Faults. IEEE Industry Applications Magazine, 7(4), pp. 26–34. doi: 10.1109/2943.930988
- Tiboni, M., Remino, C., Bussola, R. and Amici, C. (2022). A Review on Vibration-Based Condition Monitoring of Rotating Machinery. Applied Sciences, 12(3), p. 972. doi: 10.3390/app12030972
- Tran, M.-Q., Liu, M.-K., Tran, Q.-V. and Nguyen, T.-K. (2022). Effective Fault Diagnosis Based on Wavelet and Convolutional Attention Neural Network for Induction Motors. IEEE Transactions on Instrumentation and Measurement, 71, pp. 1–13. doi: 10.1109/TIM.2021.3139706
- Wang, Z., Yang, J., Ye, H. and Zhou, W. (2014). A review of permanent magnet synchronous motor fault diagnosis. In: IEEE Conference and Expo Transportation Electrification Asia-Pacific. doi: 10.1109/ITEC-AP.2014.6940870.
- Wicher, B. and Nowopolski, K. (2017). Model of ADRC speed control system for complex mechanical object with backlash. In: 22nd International Conference on Methods and Models in Automation and Robotics, Miedzyzdroje, Poland 2017, pp. 379–383. doi: 10.1109/MMAR.2017.8046857.
- Wolkiewicz, M. and Skowron, M. (2017). Diagnostic System for Induction Motor Stator Winding Faults Based on Axial Flux. Power Electronics and Drives, 2(2), pp. 137–150. doi: 10.5277/PED170204
- Yamamoto, G. K., Da Costa, C. and Sousa, J. S. (2016). A Smart Experimental Setup for Vibration Measurement and Imbalance Fault Detection in Rotating Machinery. Case Studies in Mechanical Systems and Signal Processing, 4, pp. 8–18. doi: 10.1016/j.csmssp.2016.07.001
- Yatsugi, K., Pandarakone, S. E., Mizuno, Y. and Nakamura, H. (2023). Common Diagnosis Approach to Three-Class Induction Motor Faults Using Stator Current Feature and Support Vector Machine. IEEE Access, 11, pp. 24945–24952. doi: 10.1109/ACCESS.2023.3254914
- Zhang, Y. and Wang, L. (2019). Entropy-Based Methods for Fault Diagnosis of Electric Machines. Sensors, 19(15), p. 3401. doi: 10.3390/s19153401
Language: English
Page range: 316 - 333
Submitted on: Mar 9, 2026
Accepted on: May 26, 2026
Published on: Jun 22, 2026
Published by: Wroclaw University of Science and Technology
In partnership with: Paradigm Publishing Services
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© 2026 Paweł Ewert, Bartłomiej Wicher, Tomasz Pajchrowski, published by Wroclaw University of Science and Technology
This work is licensed under the Creative Commons Attribution 4.0 License.