Graphical Representation of Flight Electro-mechanical Actuators Test Data and Systems Health Monitoring Parameters Using Lissajous Patterns Enabling Automation
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Dr A Lingamurthy
Abstract
Linear electromechanical actuators are popular in flight servo-actuation systems. Repertories of mandatory acceptance and health-monitoring tests result in the generation of large amounts of data. This poses challenges in handling the volumes of similar data sets by quality/systems health engineers, causing fatigue in making critical decisions. Attempt is made to ease such tasks by representing the data in a pictorial form to easily identify the parameters of interest. This study focuses on the use of Lissajous figures to pictorially represent the acceptance and condition monitoring test data the flight actuators. A graphical interpretation of acceptance test data represented by Lissajous patterns, aimed at the automation of test outcomes, is attempted. The extraction of system health indicators for mechanical faults in electromechanical linear actuators is also attempted. The results will help in developing expert systems taking advantage of current efficient pattern recognition and classification algorithms. This study focuses on easing the critical flight-worthiness clearance jobs of quality engineers and extracting system health parameters from test data represented graphically.
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Lissajous figures, Acceptance tests, Servo Actuators, Electro mechanical actuators, graphical analysis, systems health monitoring
Armughan H., Sufi T.G. & Abdul Q.K., (2016). A Park’s Vector Approach Using Process Monitoring Statistics of Principal Component Analysis for Machine Fault Detection, 978-1-5090-3552-6/16 IEEE.
Avoci M.U., Carratu M., Pietrosanto A., Paciello V. & Lay-Ekuakille V., (2020). Vibrations Measurement and Current Signatures for Fault Detection in Asynchronous Motor, 978-1-7281-4460-3/20 IEEE.
Babu G.S, Lingamurthy A & Sekhar A.S, (2011). Condition monitoring of brushless DC motor-based electromechanical linear actuators using motor current signature analysis, The International Journal of Condition Monitoring, Volume 1, Issue 1, pg 20-32.
Bhaskar M.R., Asish D., Babu G.S., Venugopal D, & Chattopadhyay A.K., (2015). Development of Electrical Actuation System for Thrust Vector Control, 9th National Symposium and Exhibition on Aerospace and Related Mechanisms, ARMS-2015-135.
Chenguo Y., Zhongyong Z., Yan M., Chengxiang L., Yifan L. & Guochao Q., (2015). Improved Online Monitoring Method for Transformer Winding Deformations Based on the Lissajous Graphical Analysis of Voltage and Current, IEEE Transactions On Power Delivery, Vol. 30, No. 4. doi:10.1109/TPWRD.2015.2418344.
Gurevich I.B, Yashina V.V and Ablameyko S.V (2018). Development and Experimental Investigation of Mathematical Methods for Automating the Diagnostics and Analysis of Ophthalmological Images. Pattern Recognit. Image Anal. 28, 612–636 doi:10.1134/S1054661818040120.
Hisham A.H, Al-Khazali, & Mohamad R.A, (2012). Geometrical and graphical representations analysis of Lissajous figures in rotor dynamic system, IOSR Journal of engineering, Vol 2(5) pp:971-978.
Qing Bi, Dingguo S., Jian L., (2019). Signal Correction of Linear Hall for PMSM Control System, IEEE 3rd Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), 978-1-7281-0513-0/19.
Ruijin W., Wen W., Zhanfeng C., Zhiqian S., Chuanyong W., Keqing L., Fuming H. & Bingfeng J., (2022). Modeling and compensation for dynamic hysteresis of piezoelectric actuators based on Lissajous Curve. Sensors and Actuators. Vol. 335, 113353, https://doi.org/10.1016/j.sna.2021.113353.
Sazali Y., Amir R. & Pranesh K., (2021). Simulation Analysis for Induction Motor Drive Fault Detection and Localization Under Variable Load and Speed Operation, International Scientific Forum (ISF), IOP Conf. Series: Materials Science and Engineering, doi:10.1088/1757-899X/1127/1/012024.
Shashikumar K. & Vijayakumar V., (2021). Detecting industrial motor faults with current signatures. PMID: 36398279; PMCID: PMC9634139. doi: 10.12688/f1000research.54266.1.
Tapas H, (2021). A geometry of the power flow and harmonic analysis with the Lissajous figures, International conference on sustainable energy and future electric transportation (SeFeT).
Urala B.K., Rathin R.N, Srirangaraj S., Aparajita D., Scott B., Venu G. & Krishna R., (2018). Automated Extraction of Data from Binary Phase Diagrams for Discovery of Metallic Glasses. In: Fornés, A., Lamiroy, B. (eds) Graphics Recognition. Current Trends and Evolutions. GREC. Lecture Notes in Computer Science, vol 11009. Springer, Cham. doi:10.1007/978-3-030-02284-6_1
Xiaowen L & Juncheng L, (2021). Research on the Application of Machine Learning Technology in the Automatic Recognition System of Engineering Graphics, IEEE 3rd International Conference on Civil Aviation Safety and Information Technology 716-720, doi:10.1109/ICCASIT53235.2021.9633603.