Attention mechanism-based multisensor data fusion neural network for fault diagnosis of autonomous underwater vehicles

The autonomous underwater vehicle (AUV) frequently operates in harsh underwater environments, and timely fault diagnosis of the AUV can prevent mission failure and equipment loss. Data-driven methods based on a single data source have been widely utilized for fault diagnosis of the AUV because they do not require the construction of complex mechanism models and have high fault diagnosis accuracy. However, these methods face challenges in accomplishing complex fault diagnosis tasks because the single data source provides very restricted fault features. To address this issue, an attention mechanism-based multisensor data fusion neural network (MDFNN) for AUV fault diagnosis is proposed in this work. First, a feature extraction layer based on the two-dimensional (2D) convolutional method with a 1D kernel is introduced to extract features from each sensor data separately, significantly optimizing the model architecture. Second, an efficient channel attention mechanism-based feature fusion layer is proposed to reassign weights to the features of each sensor data, enabling the model to focus more on crucial features. Finally, the fused features are input to the fully connected layers and softmax layer to realize the fault diagnosis of multisensor data. In the end, the diagnostic performance of the proposed MDFNN is evaluated utilizing real AUV experimental data. The experiment shows that the proposed MDFNN has a very fast convergence speed and 98.37% fault diagnosis accuracy, demonstrating its excellent fault diagnosis performance. The proposed MDFNN provides a generalized and simply structured fault diagnosis framework for the AUV with multiple types of sensor data, providing significant engineering value.

Automated summary

This paper proposes an attention mechanism-based multisensor data fusion neural network (MDFNN) for fault diagnosis of autonomous underwater vehicles (AUVs). The MDFNN utilizes a feature extraction layer and a feature fusion layer to optimize the model architecture and achieve high fault diagnosis accuracy.