Proposal of a model-based fault identification genetic technique for more-electric aircraft flight control EM actuators



Matteo D. L. Dalla Vedova Alfio Germanà Paolo Maggiore


In the last years, Electro-Mechanical Actuators (EMAs) are gradually replacing the older type of actuators based on the hydraulic power. In order to detect incipient failures due to a progressive wear of a primary flight command EMA, prognostics could employ several approaches; the choice of the best ones is driven by the efficacy shown in failure detection, since not all the algorithms might be useful for the proposed purpose. In other words, some of them could be suitable only for certain applications while they could not give useful results for others. Developing a prognostic
algorithm able to identify the precursors of the above mentioned EMAs faults and their degradation pattern is thus beneficial for anticipating the incoming failure and alerting the maintenance crew such to properly schedule the servomechanism replacement. The goal of this paper is to propose an innovative modelbased fault detection and identification (FDI) method, based on Genetic Algorithms (GA), able to identify symptoms alerting that an EMA component is degrading and will eventually exhibit an anomalous behavior; in particular four kinds of EMA progressive fault are considered: friction, backlash, coil short circuit and electronics fault of controller. To assess the effectiveness of the proposed technique, an appropriate simulation test environment was
developed: in particular, two MATLAB Simulink models representing the real EMA and the corresponding monitor have been used to simulate failures and evaluate the accuracy of the FDI algorithm. The results showed an adequate robustness and confidence was gained in the ability to early identify an eventual EMA malfunctioning with low risk of false alarms or missed failures. This paper aims to be a starting point to future works based on this method for PHM applications.

How to Cite

Vedova, M. D. L. D., Germanà, A., & Maggiore, P. (2016). Proposal of a model-based fault identification genetic technique for more-electric aircraft flight control EM actuators. PHM Society European Conference, 3(1).
Abstract 251 | PDF Downloads 38



Electromechanical actuator, model based prognostics, EMA, genetic algorithm, Fault identification, BLDC Motor Faults

Alamyal, M., Gadoue, S. M., & Zahawi, B. (2013). Detection of induction machine winding faults using genetic algorithm. Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED), 9th IEEE International Symposium on, Valencia, pp. 157-161.
Battipede, M., Dalla Vedova, M. D. L., Maggiore, P., & Romeo, S. (2015). Model based analysis of precursors of electromechanical servomechanisms failures using an artificial neural network, Proc. of the AIAA SciTech Modeling and Simulation Technologies Conference, January, 5-9, Kissimmee, FL.
Borello, L., Dalla Vedova, M. D. L., Jacazio, G., & Sorli, M. (2009). A Prognostic Model for Electrohydraulic Servovalves, Proc. of the Annual Conference of the Prognostics and Health Management Society, San Diego, USA.
Borello, L., & Dalla Vedova, M. D. L. (2012). A Dry Friction Model and Robust Computational Algorithm for Reversible or Irreversible Motion Transmission. International Journal of Mechanics and Control (JoMaC), vol. 13, n. 02, pp. 37-48, ISSN: 1590-8844.
Borello, L., Villero, G., & Dalla Vedova, M. D. L. (2014). Flaps Failure and Aircraft Controllability: Developments in Asymmetry Monitoring Techniques. Journal of Mechanical Science and Technology
(JMST), vol. 28, v. 11, pp. 4593-4603.
Byington, C. S., Watson, W., Edwards, D., & Stoelting, P. (2004). A Model-Based Approach to Prognostics and Health Management for Flight Control Actuators, IEEE Aerospace Conference Proceedings, USA.
Chesley, J. C. (2011). Handbook of Reliability Prediction Procedures for Mechanical Equipment. USA Navy, Naval Surface Warfare Center, NSWC Carderock Division, Bethesda, Maryland.
Çunkas, M., & Aydoğdu, O. (2010). Realization of Fuzzy Logic Controlled Brushless DC Motor Drives using Matlab/Simulink. Mathematical and Computational Applications, vol. 15, n. 02, pp. 218-229.
Dalla Vedova, M. D. L., Maggiore, P., & Pace, L. (2014) Proposal of Prognostic Parametric Method Applied to an Electrohydraulic Servomechanism Affected by Multiple Failures, WSEAS Trans. on Environment and Development, vol. 10, pp. 478-490, ISSN: 1790-5079.
Germanà, A. (2015). Genetic Algorithms for the prognostic of electro-mechanic servomechanisms for aeronautical applications, MSc Thesis, Politecnico di Torino, Italy.
Ginart, A., Brown, D., Kalgren, P., & Roemer, M. (2007). On-line Ringing Characterization as a PHM Technique for Power Drives and Electrical Machinery. Autotestcon, 2007 IEEE, September 17-20.
Ginart, A., Brown, D., Kalgren, P., & Roemer, M. (2008). Inverter Power Drive Transistor Diagnostic and Extended Operation under One-Transistor Trigger Suppression. Applied Power Electronics Conference and Exposition, 2008. APEC 2008. February 24-28.
Gökdere, L. U., Chiu, S. L., Keller, K. J., Vian, J. (2005). Lifetime control of electromechanical actuators. IEEE Aerospace Conference Proceedings. March 5-12, Big Sky, MT. doi: 10.1109/AERO.2005.1559655
Halvaei Niasar, A., Moghbelli, H., & Vahedi, A. (2009). Modelling, Simulation and Implementation of Four-Switch Brushless DC Motor Drive Based On Switching Functions. IEEE EUROCON 2009. May 18-23, St.-Petersburg, Russia.
Howse, M. (2003). All-electric aircraft, Power Engineer, vol. 17, n. 4, pp. 35-37.
Hua, J., & Zhiyong, H. (2008). Simulation of Sensorless Permanent Magnetic Brushless DC Motor Control System. Proc. of the IEEE International Conference on Automation and Logistics, September, Qingdao, China.
Kaliappan, E., & Chellamuthu, C. (2012). Simplified Modeling, Analysis and Simulation of Permanent Magnet Brushless Direct Current Motors for Sensorless Operation. American Journal of Applied Sciences, vol. 9, n. 7, pp. 1046-1054. ISSN 1546-9239
Kenjo, T,, & Nagamori, S. (2003). Brushless Motors: Advanced Theory and Modern Applications, Sogo Electronics Press, Tokyo, Japan.
Maggiore, P., Dalla Vedova, M. D. L., Pace, L., & Desando, A. (2015). Proposal of fault analysis parametric method applied to an electromechanical servomechanisms affected by failures. Intern. Journal of Prognostics and Health Management, vol. 6, n. 1. ISSN: 2153-2648.
Mitchell, M. (1996). An introduction to genetic algorithms. Cambridge, MIT press.
Quigley, R. E. J. (1993). More electric aircraft. Proceedings of Eighth Annual IEEE Applied Power Electronics Conference - APEC '93, March 7-11, San Diego, CA.
Raie, A., & Rashtchi, V. (2002). Using a genetic algorithm for detection and magnitude determination of turn faults in an induction motor. Electrical Engineering, 2002, vol. 84, n. 5, pp. 275-279
Shashidhara, S.M., & Raju, P.S. (2013). Stator Winding Fault Diagnosis of Three-Phase Induction Motor by Parks Vector Approach. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering (IJAREIEE), vol. 2, n. 7.
Todić, I., Miloš, M., & Pavišić, M. (2013). Position and speed control of electromechanical actuator for aerospace applications. Tehnicki Vjesnik, vol. 20, n. 5, pp. 853-860.
Weiss, J. (2014). Control Actuation Reliability and Redundancy for Long Duration, Underwater Vehicle Missions with High Value Payloads. Proceedings of the 2014 Underwater Intervention Conference.
Technical Papers

Similar Articles

1 2 3 4 5 6 7 8 9 > >> 

You may also start an advanced similarity search for this article.