Non-intrusive current-based fault detection of electro-mechanical actuators with brushed DC motors
DOI:
https://doi.org/10.24425/mms.2022.140040Abstract
This paper proposes data-based fault detection methods for an electromechanical actuator (EMA) with a brushed DC motor. The jam and winding short faults are considered in the study as the most prominent EMA faults. The fault detection is based on evaluating the properties of the motor current, considering the basic electromechanical parameters of EMAs. The main advantages are a non-intrusive approach utilising a commonly accessible motor current measurement, simple configurability, and the ability to detect faults under varying operation modes of EMA, including changes of speed, load, or movement profiles. The proposed methods have been evaluated with a custom testing system, and the results have proven the performance of the proposed approach to detect faults under varying operating conditions in industrial applications.References
[1] Chirico, I., Anthony, J., & Kolodziej, J. R. (2014). A Data-Driven Methodology for Fault Detection in Electromechanical Actuators. Journal of Dynamic Systems, Measurement, and Control, 136(4). https://doi.org/10.1115/1.4026835
[2] Arriola, D., & Thielecke, F. (2017). Model-based design and experimental verification of a monitoring concept for an active-active electromechanical aileron actuation system. Mechanical Systems and Signal Processing, 94, 322-345. https://doi.org/10.1016/j.ymssp.2017.02.039
[3] Di Rito, G., Schettini, F., & Galatolo, R. (2018). Model-Based Prognostic Health-Management Algorithms for the Freeplay Identification in Electromechanical Flight Control Actuators. 2018 5th IEEE International Workshop on Metrology for AeroSpace, 340-345. https://doi.org/10.1109/MetroAeroSpace.2018.8453552
[4] Balaban, E., Bansal, P., & Stoelting, P. (2009). A diagnostic approach for electro-mechanical actuators in aerospace systems. 2009 IEEE Aerospace conference, 2009. https://doi.org/10.1109/AERO.2009.4839661
[5] Ruiz-Carcel, C., & Starr, A. (2018). Data-Based Detection and Diagnosis of Faults in Linear Actuators. IEEE Transactions on Instrumentation and Measurement, 67(9), 2035-2047. https://doi.org/10.1109/tim.2018.2814067
[6] Ismail, M. A. A., Balaban, E., & Spangenberg, H. (2016). Fault detection and classification for flight control electromechanical actuators. 2016 IEEE Aerospace Conference, 1-10. https://doi.org/10.1109/AERO.2016.7500784
[7] Mazzoleni, M., Previdi, F., Scandella, M., & Pispola, G. (2019). Experimental Development of a Health Monitoring Method for Electro-Mechanical Actuators of Flight Control Primary Surfaces in More Electric Aircrafts. IEEE Access, 7, 153618-153634. https://doi.org/10.1109/ACCESS.2019.2948781
[8] Wang, C., Tao, L., Ding, Y., Lu, C., & Ma, J. (2022). An adversarial model for electromechanical actuator fault diagnosis under nonideal data conditions. Neural Computing and Applications, 34(8), 5883-5904. https://doi.org/10.1007/s00521-021-06732-x
[9] Zhao, D., Cheng, W., Gao, R. X., Yan, R., & Wang, P. (2020). Generalized Vold-Kalman Filtering for Nonstationary Compound Faults Feature Extraction of Bearing and Gear. IEEE Transactions on Instrumentation and Measurement, 69(2), 401-410. https://doi.org/10.1109/TIM.2019.2903700
[10] Yuyan, C., Jian, W., Rong, X., & Xinmin, W. (2015). Fault tree analysis of electro-mechanical actuators. 2015 34th Chinese Control Conference (CCC), 6392-6396. https://doi.org/10.1109/ChiCC.2015.7260646
[11] Stone, G. C. (2004). Electrical insulation for rotating machines: design, evaluation, aging, testing, and repair. Wiley-Interscience
[12] Westfall, P. H. (2014). Kurtosis as Peakedness, 1905 - 2014.R.I.P. The American Statistician, 68(3), 191-195. https://doi.org/10.1080/00031305.2014.917055
[13] Lee, S. B., Shin, J., Park, Y., Kim, H., & Kim, J. (2021). Reliable Flux-Based Detection of Induction Motor Rotor Faults from the Fifth Rotor Rotational Frequency Sideband. IEEE Transactions on Industrial Electronics, 68(9), 7874-7883. https://doi.org/10.1109/TIE.2020.3016241
[14] Park, Y., Choi, H., Shin, J., Park, J., Lee, S. B., & Jo, H. (2020). Airgap Flux Based Detection and Classification of Induction Motor Rotor and Load Defects During the Starting Transient. IEEE Transactions on Industrial Electronics, 67(12), 10075-10084. https://doi.org/10.1109/TIE.2019.2962470
[15] Hussain, Y., Burrow, S., Henson, L., & Keogh, P. (2020). A Review of Techniques to Mitigate Jamming in Electromechanical Actuators for Safety Critical Applications. International Journal of Prognostics and Health Management, 9. https://doi.org/10.36001/ijphm.2018.v9i3.2749
[16] De Martin, A., Jacazio, G., & Vachtsevanos, G. (2017). Windings Fault Detection and Prognosis in Electro-Mechanical Flight Control Actuators Operating in Active-Active Configuration. International Journal of Prognostics and Health Management, 8. https://doi.org/10.36001/ijphm.2017.v8i2.2633
[17] S Zhang, J., Zhan, W., & Ehsani, M. (2018). On-line diagnosis of inter-turn short circuit fault for DC brushed motor. ISA Transactions, 77, 179-187. https://doi.org/10.1016/j.isatra.2018.03.029