Fault Detection based on MCSA for a 400Hz Asynchronous Motor for Airborne Applications

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Published Nov 1, 2020
Steffen Haus Heiko Mikat Martin Nowara Surya Teja Kandukuri Uwe Klingauf Matthias Buderath

Abstract

Future health monitoring concepts in different fields of engineering require reliable fault detection to avoid unscheduled machine downtime. Diagnosis of electrical induction machines for industrial applications is widely discussed in literature. In aviation industry, this topic is still only rarely discussed. A common approach to health monitoring for electrical induction machines is to use Motor Current Signature Analysis (MCSA) based on a Fast Fourier Transform (FFT). Research results on this topic are available for comparatively large motors, where the power supply is typically based on 50Hz alternating current, which is the general power supply frequency for industrial applications.

In this paper, transferability to airborne applications, where the power supply is 400Hz, is assessed. Three phase asynchronous motors are used to analyse detectability of different motor faults. The possibility to transfer fault detection results from 50Hz to 400Hz induction machines is the main question answered in this research work. 400Hz power supply frequency requires adjusted motor design, causing increased motor speed compared to 50Hz supply frequency. The motor used for experiments in this work is a 800W motor with 200V phase to phase power supply, powering an avionic fan. The fault cases to be examined are a bearing fault, a rotor unbalance, a stator winding fault, a broken rotor bar and a static air gap eccentricity. These are the most common faults in electrical induction machines which can cause machine downtime. The focus of the research work is the feasibility of the application of MCSA for small scale, high speed motor design, using the Fourier spectra of the current signal.

Detectability is given for all but the bearing fault, although rotor unbalance can only be detected in case of severe damage level. Results obtained in the experiments are interpreted with
respect to the motor design. Physical interpretation are given in case the results differ from those found in literature for 50Hz electrical machines.

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Keywords

fault detection, aircraft systems, Bearing Faults, Unbalance, Motor Current Signature Analysis, stator winding faults, broken rotor bars, air gap eccentricity, 400 Hz power supply

References
Ayhan, B., Chow, M., & Song, M. (2006). Multiple discriminant analysis and neural-network-based monolith and partition fault-detection schemes for broken rotor bar in induction motors. Industrial Electronics, IEEE Transactions on, 53(4), 1298–1308.
Benbouzid, M., & Kliman, G. (2003). What stator current processing-based technique to use for induction motor rotor faults diagnosis? Energy Conversion, IEEE Transactions on, 18(2), 238–244.
Ben Salem, S., Bacha, K.,&Gossa, M. (2012). Induction motor fault diagnosis using an improved combination of Hilbert and Park transforms. In Electrotechnical Conference (MELECON), 2012 16th IEEE Mediterranean (pp. 1141–1146).
Blodt, M., Granjon, P., Raison, B., & Rostaing, G. (2008). Models for bearing damage detection in induction motors using stator current monitoring. Industrial Electronics, IEEE Transactions on, 55(4), 1813–1822.
El Hachemi Benbouzid, M. (2000). A review of induction motors signature analysis as a medium for faults detection. Industrial Electronics, IEEE Transactions on, 47(5), 984–993.
Fritzsche, R., & Lasch, R. (2012). An Integrated Logistics Model of Spare Parts Maintenance Planning within the Aviation Industry. In Proceedings of world academy of science, engineering and technology (Vol. 68).
Haji, M., & Toliyat, H. (2001). Pattern recognition-a technique for induction machines rotor broken bar detection. Energy Conversion, IEEE Transactions on, 16(4), 312–317.
Knotts, R. (1999). Civil aircraft maintenance and support Fault diagnosis from a business perspective. Journal of quality in maintenance engineering, 5(4), 335–348.
Lau, E., & Ngan, H. (2010). Detection of motor bearing outer raceway defect by wavelet packet transformed motor current signature analysis. Instrumentation and Measurement, IEEE Transactions on, 59(10), 2683–2690.
Marques Cardoso, A., Cruz, S., & Fonseca, D. (1999). Interturn stator winding fault diagnosis in three-phase induction motors, by Park’s vector approach. Energy Conversion, IEEE Transactions on, 14(3), 595–598.
MEHALA, N. (2010). Condition monitoring and fault diagnosis of induction motor using motor current signature analysis. Unpublished doctoral dissertation, National Institude of Technology Kurukshertra, India.
Nandi, S., Toliyat, H., & Li, X. (2005). Condition monitoring and fault diagnosis of electrical motors-a review. Energy Conversion, IEEE Transactions on, 20(4), 719–729.
Saadaoui, W., & Jelassi, K. (2008). Gearbox-induction machine bearing fault diagnosis using spectral analysis. In Computer Modeling and Simulation, 2008. EMS’08. Second UKSIM European Symposium on (pp. 347–352).
Schoen, R., Habetler, T., Kamran, F., & Bartfield, R. (1995). Motor bearing damage detection using stator current monitoring. Industry Applications, IEEE Transactions on, 31(6), 1274–1279.
Thomson, W. (2001). On-line MCSA to diagnose shorted turns in low voltage stator windings of 3-phase induction motors prior to failure. In Electric Machines and Drives Conference, 2001. IEMDC 2001. IEEE International (pp. 891–898).
Thomson, W., & Gilmore, R. (2003). Motor current signature analysis to detect faults in induction motor drives– fundamentals, data interpretation, and industrial case histories. In Proceedings of the thirty-second turbomachinery symposium (pp. 145–156).
Thomson, W., Rankin, D., & Dorrell, D. (1999). On-line current monitoring to diagnose airgap eccentricity in large three-phase induction motors-industrial case histories verify the predictions. Energy Conversion, IEEE Transactions on, 14(4), 1372–1378.
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Technical Papers