Machine learning based prognostics of on-board electromechanical actuators

Minisci, Edmondo and Dalla Vedova, Matteo D.L. and Alimhillaj, Parid and Baldo, Leonardo and Maggiore, Paolo; Guxho, Genti and Kosova Spahiu, Tatjana and Prifti, Valma and Gjeta, Ardit and Xhafka, Eralda and Sulejmani, Anis, eds. (2024) Machine learning based prognostics of on-board electromechanical actuators. In: Proceedings of the Joint International Conference. Lecture Notes on Multidisciplinary Industrial Engineering . Springer, ALB, pp. 148-159. ISBN 9783031489334 (https://doi.org/10.1007/978-3-031-48933-4_15)

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Abstract

This paper presents a novel machine learning-based prognostic approach for on-board electromechanical actuators. The study is centered around overcoming the limitations of model-based prognostic frameworks that rely on expensive optimization processes. Machine learning techniques were employed to map system signal characteristics directly into parameters related to fault simulation. A first test, utilizing only five of eight implemented fault types, demonstrates a highly promising potential of artificial neural networks to predict and detect faults with minimal error. A second test expands the investigation to include all fault types and provides an analysis of the model’s robustness, error rates, and computational costs. The practical outcome of the work is a viable real-time solution for fault detection and characterization in electromechanical actuators, highlighting the efficiency and effectiveness of machine learning techniques for industrial applications.

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