Machine Learning Based Prognostics of On-Board Electromechanical Actuators

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.