Development of a Data-driven Condition-Based Maintenance Methodology Framework for an Advanced Jet Trainer

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Jun 27, 2024
Leonardo Baldo

Abstract

Since their introduction more than 20 years ago, PHM strate- gies for aerospace equipment have gone a long way, enabling operators and Original Equipment Manufacturers (OEM) to monitor their assets, track down abnormal behaviors and plan maintenance action in advance. On the other hand, the tran- sition from PHM strategies using simulated data to solutions utilizing real-life operational data is consistently prone to sig- nificant challenges and demands. This doctoral thesis aims to develop a PHM/CBM framework applied to a Electro-Hydraulic Actuators (EHAs) leveraging real in-service fleet data. In this paper, the first steps of the research project are presented.

How to Cite

Baldo, L. (2024). Development of a Data-driven Condition-Based Maintenance Methodology Framework for an Advanced Jet Trainer. PHM Society European Conference, 8(1), 5. https://doi.org/10.36001/phme.2024.v8i1.3943
Abstract 227 | PDF Downloads 182

##plugins.themes.bootstrap3.article.details##

Keywords

PHM, EHA, Flight controls, Actuator, CBM, Data-driven, Flight Data, In-service data, Trainer Aircraft, Electro-Hydraulic Actuators

References
Autin, S., De Martin, A., Jacazio, G., Socheleau, J., & Vachtsevanos, G. (2021). Results of a Feasibility Study of a Prognostic System for Electro-Hydraulic Flight Control Actuators. International Journal of Prognostics and Health Management, 12. doi: 10.36001/ijphm.2021.v12i3.2935
Baldo, L., De Martin, A., Sorli, M., & Terner, M. (2023). Condition-based-maintenance for fleet man- agement. In Aerospace science and engineering - iii aerospace phd-days (pp. 57–60). doi: https://doi.org/10.21741/9781644902677-9
Bertolino, A. C., Gentile, R., Jacazio, G., Marino, F., & Sorli, M. (2018). Ehsa primary flight controls seals wear degradation model. In Asme international mechanical engineering congress and exposition (Vol. 52002, p. V001T03A024). doi: 10.1115/IMECE2018-87080
Byington, C., Watson, M., & Edwards, D. (2004). Data-driven neural network methodology to remaining life predictions for aircraft actuator components. In 2004 IEEE Aerospace Conference Proceedings (IEEE Cat. No.04TH8720) (Vol. 6, pp. 3581–3589). (ISSN: 1095- 323X) doi: 10.1109/AERO.2004.1368175
Chao, Q., Shao, Y., Liu, C., & Yang, X. (2023). Health evaluation of axial piston pumps based on density weighted support vector data description. Reliability Engineering & System Safety, 237, 109354. doi: 10.1016/j.ress.2023.109354
Chiavaroli, P., De Martin, A., Evangelista, G., Jacazio, G., & Sorli, M. (2018). Real time loading test rig for flight control actuators under phm experimentation. In Asme international mechanical engineering congress and ex- position (Vol. 52002). doi: 10.1115/IMECE201886967
Cui, Z., Jing, B., Jiao, X., Huang, Y., & Wang, S. (2023). The Integrated-Servo-Actuator Degradation Prognosis Based on the Physical Model Combined With Data-Driven Approach. IEEE Sensors Journal, 23(9), 9370– 9381. doi: 10.1109/JSEN.2023.3248323
De Martin, A., Jacazio, G., & Sorli, M. (2022). Evaluation of Different PHM Strategies on the Performances of a Prognostic Framework for Electro-Hydraulic Actuators for Stability Control Augmentation Systems. In Annual Conference of the PHM Society (Vol. 14). (Number: 1) doi: https://doi.org/10.36001/phmconf.2022.v14i1.3289
Esperon-Miguez, M., John, P., & Jennions, I. K. (2013). A review of Integrated Vehicle Health Management tools for legacy platforms: Challenges and opportuni- ties. Progress in Aerospace Sciences, 56, 19–34. doi: https://doi.org/10.1016/j.paerosci.2012.04.003
Guo, R., & Sui, J. (2020). Remaining Useful Life Prognostics for the Electrohydraulic Servo Actuator Using Hellinger Distance-Based Particle Filter. IEEE Transactions on Instrumentation and Measurement, 69(4), 1148–1158. doi: 10.1109/TIM.2019.2910919
Hess, A., & Fila, L. (2002). The Joint Strike Fighter (JSF) PHM concept: Potential impact on aging aircraft problems. In Proceedings, IEEE Aerospace Conference (Vol. 6, pp. 6–6).
Iyaghigba, S. D., Ali, F., & Jennions, I. K. (2023). A Review of Diagnostic Methods for Hydraulically Powered Flight Control Actuation Systems. Machines, 11(2), 165. doi: 10.3390/machines11020165
Iyaghigba, S. D., Petrunin, I., & Avdelidis, N. P. (2024). Modeling a hydraulically powered flight control actuation system. Applied Sciences, 14(3), 1206.
Kannemans, H., & Jentink, H. W. (2002). A Method to Derive the Usage of Hydraulic Actuators From Flight Data. In Icas 2002 congress.
Kordestani, M., Samadi, M. F., & Saif, M. (2020). A New Hybrid Fault Prognosis Method for MFS Systems Based on Distributed Neural Networks and Recursive Bayesian Algorithm. IEEE Systems Journal, 14(4), 5407–5416. doi: 10.1109/JSYST.2020.2986162
Kosova, F., & Unver, H. O. (2023). A digital twin framework for aircraft hydraulic systems failure detection using machine learning techniques. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 237(7), 1563–1580. (Publisher: IMECHE) doi: 10.1177/09544062221132697
Liu, H., Zhang, J., & Lu, C. (2015). Performance degradation prediction for a hydraulic servo system based on elman network observer and gmm–svr. Applied Mathematical Modelling, 39(19), 5882–5895.
Lu, C., Yuan, H., & Ma, J. (2018). Fault detection, diagnosis, and performance assessment scheme for multiple redundancy aileron actuator. Mechanical Systems and
Signal Processing, 113, 199–221.
Mi, J., & Huang, G. (2023). Dynamic Prediction of Performance Degradation Characteristics of Direct-Drive Electro-Hydraulic Servo Valves. Applied Sciences, 13(12), 7231. doi: 10.3390/app13127231
Rodrigues, L., Yoneyama, T., & Nascimento Jr, C. (2012). How aircraft operators can benefit from PHM techniques. In Ieee aerospace conference proceedings. doi: 10.1109/AERO.2012.6187376
S5000F. (2023). S5000f - international specification for in- service data feedback (Vol. Issue No. 3.1; Tech. Rep. No. S5000F-B6865-05000-00).
Schoenmakers, L. (2020). Condition-based Maintenance for the RNLAF C-130H(-30) Hercules (Unpublished doctoral dissertation). Eindhoven University of Technol- ogy.
Shanbhag, V. V., Meyer, T. J. J., Caspers, L. W., & Schlanbusch, R. (2021). Failure Monitoring and Predictive Maintenance of Hydraulic Cylinder—State-of-the-Art Review. IEEE/ASME Transactions on Mechatronics, 26(6), 3087–3103. doi: 10.1109/TMECH.2021.3053173
Shen, K., & Zhao, D. (2023). A Fault Diagnosis Method under Data Imbalance Based on Generative Adversarial Network and Long Short-Term Memory Algorithms for Aircraft Hydraulic System. Aerospace, 10(2), 164. doi: 10.3390/aerospace10020164
Smith, G., Schroeder, J., Navarro, S., & Haldeman, D. (1997). Development of a prognostics and health management capability for the Joint Strike Fighter. In 1997 IEEE Autotestcon Proceedings AUTOTESTCON ’97. IEEE Systems Readiness Technology Conference (pp. 676–682). doi: 10.1109/AUTEST.1997.643994
Soudbakhsh, D., & Annaswamy, A. M. (2017). Prognostics and health monitoring of electro-hydraulic systems. In Dynamic systems and control conference (Vol. 58288). doi: 10.1115/DSCC2017-5392
Vianna, W. O., & Malere, J. P. P. (2014). Aircraft hydraulic system leakage detection and servicing recommendations method. In Annual conference of the phm society (Vol. 6).
Vogl, G., Weiss, B., & Donmez, M. A. (2014). Standards for Prognostics and Health Management (PHM) Techniques within Manufacturing Operations. In An- nual conference of the phm society (Vol. 6). doi: https://doi.org/10.36001/phmconf.2014.v6i1.2503
Zhong, Q., Xu, E., Shi, Y., Jia, T., Ren, Y., Yang, H., & Li, Y. (2023). Fault diagnosis of the hydraulic valve using a novel semi-supervised learning method based on multi-sensor information fusion. Mechanical Systems and Signal Processing, 189, 110093. doi: 10.1016/j.ymssp.2022.110093
Section
Doctoral Symposium