Application of blind source separation to the health monitoring of electrical and mechanical faults in a linear actuator

Ryan Michaud; Romain Breuneval; Emmanuel Boutleux; Julien Huillery; Guy Clerc; Badr Mansouri; Ryan Michaud; Romain Breuneval; Emmanuel Boutleux; Julien Huillery; Guy Clerc; Badr Mansouri

doi:10.3934/ElectrEng.2019.4.328

AIMS Electronics and Electrical Engineering

2019, Volume 3, Issue 4: 328-346. doi: 10.3934/ElectrEng.2019.4.328

Previous Article Next Article

Research article Special Issues

Application of blind source separation to the health monitoring of electrical and mechanical faults in a linear actuator

1.
Université de Lyon, UCB Lyon 1, CNRS, AMPERE, F-69100, Villeurbanne, France
2.
Université de Lyon, Ecole Centrale LYON, CNRS, AMPERE, F-69130, Ecully, France
3.
Safran Electronics & Defense, Avionics Division, Massy, France

Received: 27 June 2019 Accepted: 15 October 2019 Published: 01 November 2019

This paper proposes an automated fault isolation and diagnostic chain for the health monitoring of a linear actuator composed of a roller screw driven by a permanent magnet synchronous motor. Four health conditions are considered and diagnosed: the healthy condition, a short circuit in the stator windings, a mechanical backlash in the roller screw, and the combination of both faults. In order to separate the fault signatures, empirical mode decomposition is applied to the motor current, followed by independent component analysis, automatic isolation of the fault signatures, and a classification step for the diagnosis. The novelty proposed consists of an automatic processing of the independent components to isolate the effects of the short-circuit from the effects of the backlash. This isolation step, in contrast to earlier works, requires no human intervention to select signals of interest, making it suitable to real-time onboard diagnostics. Furthermore, results show that independent component analysis occupies an important role in the diagnosis: its omission leads to a reduction in the diagnostic performance of the classifier as well as a reduction in measures of class separability.
- electrical actuators,
- fault detection,
- health monitoring,
- blind source separation,
- empirical mode decomposition,
- independent component analysis
Citation: Ryan Michaud, Romain Breuneval, Emmanuel Boutleux, Julien Huillery, Guy Clerc, Badr Mansouri. Application of blind source separation to the health monitoring of electrical and mechanical faults in a linear actuator[J]. AIMS Electronics and Electrical Engineering, 2019, 3(4): 328-346. doi: 10.3934/ElectrEng.2019.4.328

Related Papers:

Abstract

This paper proposes an automated fault isolation and diagnostic chain for the health monitoring of a linear actuator composed of a roller screw driven by a permanent magnet synchronous motor. Four health conditions are considered and diagnosed: the healthy condition, a short circuit in the stator windings, a mechanical backlash in the roller screw, and the combination of both faults. In order to separate the fault signatures, empirical mode decomposition is applied to the motor current, followed by independent component analysis, automatic isolation of the fault signatures, and a classification step for the diagnosis. The novelty proposed consists of an automatic processing of the independent components to isolate the effects of the short-circuit from the effects of the backlash. This isolation step, in contrast to earlier works, requires no human intervention to select signals of interest, making it suitable to real-time onboard diagnostics. Furthermore, results show that independent component analysis occupies an important role in the diagnosis: its omission leads to a reduction in the diagnostic performance of the classifier as well as a reduction in measures of class separability.

References

[1]	Zong W, Wan F, Wei Y (2017) Real-time monitoring for the actuator mechanism of the aileron. IEEE Prognostics and System Health Management Conference (PHM-Harbin) 2017: 1-5.
[2]	Di Rito G, Schettini F, Galatolo R (2018) Model-Based Prognostic Health-Management Algorithms for the Freeplay Identification in Electromechanical Flight Control Actuators. 5th IEEE International Workshop on Metrology for AeroSpace (MetroAeroSpace), 340-345.
[3]	Breuneval R, Clerc G, Nahid-Mobarakeh B, et al. (2017) Hybrid diagnosis of intern-turn short-circuit for aircraft applications using SVM-MBF. IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 1-6.
[4]	Hyvärinen A, Karhunen J, Oja E (2001) Independent Component Analysis. 1st ed. New York: Wiley-Interscience.
[5]	Lin J and Zhang A (2005) Fault feature separation using wavelet-ICA filter. NDT & E International 38: 421-427.
[6]	Mijović B, De Vos M, Gligorijević I, et al. (2010) Source Separation From Single-Channel Recordings by Combining Empirical-Mode Decomposition and Independent Component Analysis. IEEE T Bio-Med Eng 57: 2188-2196. doi: 10.1109/TBME.2010.2051440
[7]	Xing H and Hou J (2009) A Noise Elimination Method for ECG Signals. 2009 3rd International Conference on Bioinformatics and Biomedical Engineering, 1-3.
[8]	Papageorgiou D, Blanke M, Niemann HH, et al. (2019) Robust Backlash Estimation for Industrial Drive-Train Systems-Theory and Validation. IEEE T Contr Syst T.
[9]	Chen C, Liu Z, Zhang Y, et al. (2016) Actuator Backlash Compensation and Accurate Parameter Estimation for Active Vibration Isolation System. IEEE T Ind Electron 63: 1643-1654. doi: 10.1109/TIE.2015.2497664
[10]	Mansouri B, Piaton J, Guyamier A (2016) The backlash gap size estimation for electromechanical actuator in an operational behavior. PHME.
[11]	Fedotov O, Zhdanov A, Morozov V (2018) Experimental Determination of Kinematic Error for Actuators with Roller-Screw Mechanism. IEEE International Russian Automation Conference (RusAutoCon), 1-5.
[12]	Zheng S, Fu Y, Zhang Z, et al. (2018) Research on Detect Method for Transmission Accuracy and Efficiency of Planetary Roller Screw Pair. IEEE International Conference on Mechatronics and Automation (ICMA), 1400-1404.
[13]	Lagerberg A and Egardt B (2007) Backlash estimation with application to automotive powertrains. IEEE T Contr Syst T 15: 483-493. doi: 10.1109/TCST.2007.894643
[14]	Breuneval R, Clerc G, Nahid-Mobarakeh B, et al. (2016) Identification of a roller screw for diagnosis of flight control actuator. In: 2016 IEEE International Conference on Prognostics and Health Management (ICPHM), 1-8.
[15]	Karam W, Mare JC (2009) Modelling and simulation of mechanical transmission in roller screw electromechanical actuators. Aircr Eng Aerosp Tec 81: 288-298. doi: 10.1108/00022660910967273
[16]	Leboeuf N, Nahid-Mobarakeh B, Takorabet N, et al. (2011) Modeling of PM Synchronous Machines Under Inter-turn Fault.
[17]	Leboeuf N, Boileau T, Nahid-Mobarakeh B, et al. (2012) On Inductance Calculations in PM Motors Under Fault Conditions. IEEE T Magn 48: 2605-2616. doi: 10.1109/TMAG.2012.2197402
[18]	Comon P (1994) Independent component analysis, A new concept? Signal Process 36: 287-314. doi: 10.1016/0165-1684(94)90029-9
[19]	Comon P and De Lathauwer L (2010) Algebraic identification of under-determined mixtures. In: Handbook of Blind Source Separation, 325-365.
[20]	Moreau E and Comon P (2010) Contrasts. In: Handbook of Blind Source Separation, 65-105.
[21]	Hyvärinen A (1999) Fast and robust fixed-point algorithms for independent component analysis., IEEE T Neural Network 10: 626-634. doi: 10.1109/72.761722
[22]	Hyvärinen A (2019) FastICA Matlab Package [Accessed: 30-Sept-2019]. Available from: http://research.ics.aalto.fi/ica/fastica/.
[23]	Torres ME, Colominas MA, Schlotthauer G, et al. (2011) A complete ensemble empirical mode decomposition with adaptive noise. IEEE international conference on acoustics, speech and signal processing (ICASSP), 4144-4147.
[24]	Huang NE, Shen Z, Long SR, et al. (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences 454: 903-995. doi: 10.1098/rspa.1998.0193
[25]	Rao BKN (1996) Handbook of Condition Monitoring. 1st ed edition. Oxford, UK: Elsevier.
[26]	Kudo M and Sklansky J (2000) Comparison of algorithms that select features for pattern classifiers. Pattern Recogn 33: 25-41. doi: 10.1016/S0031-3203(99)00041-2

Reader Comments

Your name:*

Email:*
© 2019 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)