Fault Diagnosis for Underactuated Surface Vessel

In recent years deep neural networks have achieved state-of-the-art accuracy at classifying the running state of a robot. Yet we propose a composite learning model (CLM) that combines the strength of broad learning and conventional deep learning techniques to identify the fault types of underactuated surface vessels (USV). Considering the measurement noises in training and testing data, we develop a deep sparse auto-encoder (DSAE) stacked by denoising auto-encoder (DAE) and contractive auto-encoders (CAEs). To further reduce the computation time, a modified broad learning system (BLS) based classifier is developed, and the input layer receives the signal from the top layer of DSAE. We use the output of the classifier as feedback. Meanwhile value iterative (VI) based adaptive dynamic programming (ADP) is employed to calculate the near-optimal increment of connection weight. Finally, we validate the developed approach by experiments using simulation data of USV that compares the proposed CLM with the standard BLS and conventional deep learning methods.