Model-free fault diagnosis for autonomous underwater vehicles using sequence Convolutional Neural Network

The AUV must be capable of fault diagnosis if it is to perform tasks in complex environments without human assistance. However, the current fault diagnosis methods for AUV lack of manual experience and accuracy, leading to the lack of fault handling capacity. Different from the traditional model-based fault diagnosis, we propose a new model-free fault diagnosis method characterized by a deep learning-based algorithm, which is a new Sequence Convolutional Neural Network (SeqCNN) that learns the patterns between state data and fault type. More specifically, the proposed SeqCNN aims to extract global feature and local feature from state data and classify the extracted information into different fault types, and can convert two-stage diagnosis mode into a single-stage one. Compared to the traditional model-based diagnosis, it can significantly reduce the time-consuming burden, simplify the diagnosis procedure and improve the efficiency. The effectiveness of SeqCNN was validated by a practical experiment on a small quadrotor AUV 'Haizhe'. The results indicate that the proposed SeqCNN can solve the problem of fault detection and fault isolation in single-stage diagnosis mode and that its accuracy is far superior to that of other deep learning diagnosis algorithms.