Anomaly detection in power inverters of electromechanical actuators based on convolutional neural network and long short-term memory cells

In new generation aircraft, traditional hydromechanical and electrohydraulic actuators of flight control systems are replaced by Electro-Mechanical Actuators (EMAs). Ensuring the functionality of an EMA requires to monitor the health state of its components and promptly detect anomalies. This work develops an anomaly detection method for power inverters, which are among the most critical components of EMAs. It is based on a signal reconstruction model trained to reproduce the values of the signal expected under normal conditions. The cumulative Z-Score of the residuals between reconstructed and measured signals is used as anomaly indicator. The signal reconstruction model combines a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) cells. The CNN enables the extraction of features representative of the system health state from the multidimensional time-series of the measured signals of voltage, motor angular speed and motor position. The LSTM cells allows capturing the complex, non-linear temporal dynamics of the extracted features. The anomaly detection method is validated by considering sensor faults and the degradation of the inverter Metal-Oxide-Semiconductor FieldEffect Transistor (MOSFET).