Fault Diagnosis And Prognosis For Electromechanical Actuators And Sensors

This article discusses the use of deep learning methods in fault diagnosis and prediction for electromechanical actuators (EMA) and sensors in the aerospace industry. Traditional machine learning techniques are limited in their ability to process raw data, while deep learning can automatically learn abstract representation features. The Special Issue, “Fault Diagnosis and Prognosis for Electromechanical Actuators and Sensors”, seeks original research articles on DL-based fault diagnosis, prediction, and health management using EMAs and sensors.