A fault diagnosis method with multi-source data fusion based on hierarchical attention for AUV

Abstract

Multi-source data fusion is an important method to improve the performance of Autonomous Underwater Vehicle (AUV) fault diagnosis. However, most of the current fault diagnosis methods are based on a single data source or treat multi-source data as single. Firstly, we demonstrate the necessity of multi-source data fusion and propose a universal data hierarchy. Then, a hierarchical attention based multi-source data fusion method is proposed for fault diagnosis (HAMFD). The method consists of an encoder–decoder network, a fusion network stacked with encoders and attention mechanisms, and a fault recognition method based on attention distribution. The fusion network uses the encoder and hierarchical attention to extract the deep features, and fuse the features hierarchically. We use the multi-layer attention distribution to explain the fault evaluation and realize fault recognition. A random mask fusion strategy is designed for redundancy and a feature orthogonalization method is proposed for the strong coupling among multiple data sources. The proposed method is validated on the monitoring data of Qianlong-2 AUV obtained during the sea trial in the South China Sea. The fault detection rate is more than 98%, the recognition rate is about 100% for strong faults, and more than 90% for other faults.