Machine Learning with Echo State Networks for Automated Fault Diagnosis in Small Unmanned Aircraft Systems

Abstract

Echo State Network (ESN) is one of machine learning methods that can be used to detect anomalies in sensor readings. The method predicts output signals, from which a prediction error can be created. To enable faulttolerant control, ESN needs to be combined with a robust fault estimation method. Indeed, identifying the source of the faults, whether coming from sensors or actuators, is crucial in safety-critical Unmanned Aircraft Systems (UAS), since it will determine proper control actions when the faults occur. This paper presents a novel method to combine sensor anomaly detection using ESN with actuator fault estimation using adaptive extended Kalman filter (AEKF). Numerical results show the benefit of using the cascaded algorithm in a noisy environment. Furthermore, the presented method is validated using a hexacopter with actuator faults in indoor experiments.

Aircraft Actuator Performance Analysis Based on Dynamic Neural Network

“…So, to increase the diagnostic reliability of the proposed fault analysis technique, current can be used with vibration signals as an additional fault indicator, especially for critical applications [2]. Te current sensor is still separated from power safety circuits, so it does not necessarily refect other power system costs [3][4][5]. To extract valuable features, DTCWT is a suitable method for analyzing motor signals to provide features in the timefrequency domain under diferent speed and load conditions.…”