Analysis of fault-induced electromechanical disturbance effect in a closed-loop system
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Abstract
This paper examines whether motor current can provide a useful proxy for bearing faults in a closed-loop drive system. In such systems, the controller continuously adjusts current to maintain the operating point, which can hide small fault-related increases in losses and make overall current consumption a weak health indicator. To address this problem, current signatures from the Paderborn University bearing dataset were analyzed using 23 indicators covering global current level, band-limited residual ripple, envelope-based features, and classical sideband measures. These indicators were ranked according to class separability, stability in healthy measurements, and robustness to residual operating-condition variation. Their physical relevance was further explored by predicting selected current indicators from vibration features. The analysis shows that better discrimination is obtained when the analysis is shifted from total current level to residual ripple content, where fault-induced effects are significant enough to be visible and distinguishable from noise. Among the tested features, the band 105-2000 Hz features consistently provided the best separation between healthy and faulty bearings across 4 operating conditions. These results suggest that, for closed-loop drives, band-limited current features are promising monitoring inputs for sensor-light diagnostics.
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motor current signature analysis, closed-loop drives, residual current ripple, fault-induced losses, feature extraction
Ackerman, K. (2023). Life cycle costs wastewater. Retrieved from https://www.scribd.com/document/287097804/Life-Cycle-Costs-Wastewater. Accessed: 2025-12-12.
Bishop, T. H. (2024). EASA AEMT study of motor repair impact on efficiency of premium efficiency (IE3) motors. In P. Radgen & P. Bertoldi (Eds.), Energy efficiency in motor systems (pp. 23–39). Cham: Springer Nature Switzerland.
Bravo-Imaz, I., Davari Ardakani, H., Liu, Z., García-Arribas, A., Arnaiz, A., & Lee, J. (2017). Motor current signature analysis for gearbox condition monitoring under transient speeds using wavelet analysis and dual-level time synchronous averaging. Mechanical Systems and Signal Processing, 94, 73–84. doi: https://doi.org/10.1016/j.ymssp.2017.02.011
Brkovic, A., Gajic, D., Gligorijevic, J., Savic-Gajic, I., Georgieva, O., & Di Gennaro, S. (2017). Early fault detection and diagnosis in bearings for more efficient operation of rotating machinery. Energy, 136, 63–71.
Chen, J., Hu, W., Cao, D., Zhang, Z., Chen, Z., & Blaabjerg, F. (2023). A meta-learning method for electric machine bearing fault diagnosis under varying working conditions with limited data. IEEE Transactions on Industrial Informatics, 19(3), 2552–2564. doi: 10.1109/TII.2022.3165027
Garcia, M., Panagiotou, P. A., Antonino-Daviu, J. A., & Gyftakis, K. N. (2019). Efficiency assessment of induction motors operating under different faulty conditions. IEEE Transactions on Industrial Electronics, 66(10), 8072–8081.
Gritli, Y., Bellini, A., Rossi, C., Casadei, D., Filippetti, F., & Capolino, G.-A. (2017). Condition monitoring of mechanical faults in induction machines from electrical signatures: Review of different techniques. In 2017 IEEE 11th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED) (pp. 77–84). doi: 10.1109/DEMPED.2017.8062337
Jachymczyk, U., Knap, P., & Lalik, K. (2025). Improved intelligent condition monitoring with diagnostic indicator selection. Sensors, 25(1).
Kilickaya, S., & Eren, L. (2025). Bearing fault detection in adjustable speed drives via self-organized operational neural networks. Electrical Engineering, 107, 4503–4515. doi: 10.1007/s00202-024-02764-3
Korolev, N. (2025). Analytical diagnostic and control system of energy and mechanical efficiency of electric drives. Energies, 18(9).
Lessmeier, C., Kimotho, J. K., Zimmer, D., & Sextro, W. (2016). Condition monitoring of bearing damage in electromechanical drive systems by using motor current signals of electric motors: A benchmark data set for data-driven classification. In PHM Society European Conference (Vol. 3). doi: 10.36001/phme.2016.v3i1.1577
Liu, Y., & Bazzi, A. M. (2017). A review and comparison of fault detection and diagnosis methods for squirrel-cage induction motors: State of the art. ISA Transactions, 70, 400–409. doi: https://doi.org/10.1016/j.isatra.2017.06.001
Martin-Diaz, I., Morinigo-Sotelo, D., Duque-Perez, O., Arredondo-Delgado, P., Camarena-Martinez, D., & Romero-Troncoso, R. (2017). Analysis of various inverters feeding induction motors with incipient rotor fault using high-resolution spectral analysis. Electric Power Systems Research, 152, 18–26. doi: https://doi.org/10.1016/j.epsr.2017.06.021
Randall, R. B. (2011). Vibration-based condition monitoring: Industrial, automotive and aerospace applications. Chichester, UK: John Wiley & Sons.
Santer, P., Reinhard, J., Schindler, A., & Graichen, K. (2025). Detection of localized bearing faults in PMSMs by means of envelope analysis and wavelet packet transform using motor speed and current signals. Mechatronics, 106, 103294. doi: https://doi.org/10.1016/j.mechatronics.2025.103294
Sharmaa, V., & Parey, A. (2016). A review of gear fault diagnosis using various condition indicators. Elsevier, 144, 253–263.
Son, K. L. (1982). Monitorowanie stanu maszyn. Soborg, Denmark: Wydawnictwo Brüel & Kjær.
Sousa Santos, V., Cabello Eras, J. J., Sagastume Gutierrez, A., & Cabello Ulloa, M. J. (2019). Assessment of the energy efficiency estimation methods on induction motors considering real-time monitoring. Measurement, 136, 237–247. doi: https://doi.org/10.1016/j.measurement.2018.12.080
Wagner, T., & Sommer, S. (2021). Feature-based bearing fault detection with phase current sensor signals under different operating conditions. In PHM Society European Conference (Vol. 6). doi: 10.36001/phme.2021.v6i1.2852
Yang, T., Pen, H., Wang, Z., & Chang, C. S. (2016). Feature knowledge-based fault detection of induction motors through the analysis of stator current data. IEEE Transactions on Instrumentation and Measurement, 65(3), 549–558. doi: 10.1109/TIM.2015.2498978
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