WSEAS Transactions on Electronics

Print ISSN: 1109-9445
E-ISSN: 2415-1513

Volume 8, 2017

Notice: As of 2014 and for the forthcoming years, the publication frequency/periodicity of WSEAS Journals is adapted to the 'continuously updated' model. What this means is that instead of being separated into issues, new papers will be added on a continuous basis, allowing a more regular flow and shorter publication times. The papers will appear in reverse order, therefore the most recent one will be on top.

Electromechanical Actuators Affected by Multiple Failures: Prognostic Method Based on Wavelet Analysis Techniques

AUTHORS: M. D. L. Dalla Vedova, N. Lampariello, P. Maggiore

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ABSTRACT: Incipient failures of electromechanical actuators (EMA) of primary flight command, especially if related to progressive evolutions, can be identified with the employment of several different approaches. A strong interest is expected by the development of prognostic algorithms capable of identifying precursors of EMA progressive failures: indeed, if the degradation pattern is properly identified, it is possible to trig an early alert, leading to proper maintenance and servomechanism replacement. Given that these algorithms are strictly technology-oriented, they may show great effectiveness for some specific applications, while they could fail for other applications and technologies: therefore, it is necessary to conceive the prognostic method as a function of the considered failures. This work proposes a new prognostic strategy, based on artificial neural networks, able to perform the fault detection and identification of two EMA motor faults (i.e. coil short circuit and rotor static eccentricity). In order to identify a suitable data set able to guarantee an affordable ANN classification, the said failures precursors are properly pre-processed by means of Discrete Wavelet Transform extracting several features: in fact, these wavelets result very effective to detect fault condition, both in time domain and frequency domain, by means of the change in amplitude and shape of its coefficients. A simulation test bench has been developed for the purpose, demonstrating that the method is robust and is able to early identify incoming failures, reducing the possibility of false alarms or non-predicted problems.

KEYWORDS: Artificial Neural Network (ANN), BLDC Motor Failures, Electromechanical Actuator (EMA), Fault Detection/Identification (FDI) Algorithm, Prognostics, Wavelet


[1] G. Vachtsevanos, F. Lewis, M. Roemer, A. Hess, B. Wu, Intelligent Fault Diagnosis and Prognosis for Engineering Systems, Wiley, 2006. ISBN: 978-0-471-72999-0.

[2] D. W Brown, G. Vachtsevanos, A prognostic health management based framework for faulttolerant control, Proceedings of the Annual Conference of the Prognostics and Health Management Society 2011, Canada, 2011.

[3] C. S. Byington, W. Watson, D. Edwards, P. Stoelting, A Model-Based Approach to Prognostics and Health Management for Flight Control Actuators, IEEE Aerospace Conference Proceedings, 2014.

[4] P. Maggiore, M. D. L. Dalla Vedova, L. Pace, A Desando, Evaluation of the correlation coefficient as a prognostic indicator for electromechanical servomechanism failures, International Journal of Prognostics and Health Management, Vol.6, No.1, 2015.

[5] D. Belmonte, M. D. L. Dalla Vedova, P. Maggiore, Electromechanical servomechanisms affected by motor static eccentricity: proposal of fault evaluation algorithm based on spectral analysis techniques, Safety and Reliability of Complex Engineered Systems, Proceedings of ESREL 2015, CRC Press, 2015, pp. 2365-2372.

[6] I. Todić, M. Miloš M. Pavišić, Tehnicki Vjesnik 20, 2013, pp. 853-860.

[7] Kim B.W., Kim K.T., Hur J., Simplified impedance modeling and analysis for inter-turn fault of IPM-type BLDC motor, Journal of Power Electronics, Vol.12, 2012, pp. 10-18. ISSN: 1598-2092

[8] Farooq J.A., Djerdir A., Miraoui A., Modelling and simulation of stator winding inter-turn faults in permanent magnet synchronous motors, COMPEL - The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, Vol.27, No.4, 2008, pp. 887-896. ISSN: 0332-1649

[9] A. Ginart, D. Brown, P. Kalgren, M. Roemer, On-line Ringing Characterization as a PHM Technique for Power Drives and Electrical Machinery, Autotestcon, (IEEE), 2007.

[10] A. Ginart, D. Brown, P. Kalgren, M. Roemer, Inverter Power Drive Transistor Diagnostic and Extended Operation under One-Transistor Trigger Suppression, Proceedings of the Applied Power Electronics Conference and Exposition (APEC), 2008.

[11] E. E Ngu, K. Ramar, R. Montano, V. Cooray, Fault characterisation and classification using wavelet and fast Fourier transforms WSEAS Transactions on Signal Processing, Vol.4, 2008, pp. 398-408.

[12] J. Rafiee, P. Tse, A. Harifi, and M. Sadeghi, A novel technique for selecting mother wavelet function using an intelligent fault diagnosis system, Expert Systems with Applications, Vol.36, N0.3, 2009, pp. 4862-4875.

[13] W. K. Ngui, M. S. Leong, L. M. Hee, A. M. Abdelrhman, Wavelet Analysis: Mother Wavelet Selection Methods, Applied Mechanics and Materials, Vol.393, 2013, pp. 953-958.

[14] A. Phinyomark, A. Nuidod, P. Phukpattaranont, C. Limsakul, Feature Extraction and Reduction of Wavelet Transform Coefficients for EMG Pattern Classification, Electronics And Electrical Engineering, Vol.112, No.6, 2012, pp- 27-32.

[15] B. Sharma, K. Venugopalan, Comparison of neural network training functions for Hematoma classification in brain CT images, IOSR Journal of Computer Engineering, Vol.16, No.1, 2014, pp. 31-35.

[16] MATLAB® Neural Network Toolbox™,, The MathWorks, Inc., 2017.

WSEAS Transactions on Electronics, ISSN / E-ISSN: 1109-9445 / 2415-1513, Volume 8, 2017, Art. #4, pp. 21-26

Copyright © 2017 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0

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