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.

中文翻译：

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基于注意力机制的多传感器数据融合神经网络用于自主水下航行器故障诊断

自主水下航行器（AUV）经常在恶劣的水下环境中作业，及时对AUV进行故障诊断可以防止任务失败和设备损失。基于单一数据源的数据驱动方法由于不需要构建复杂的机构模型且具有较高的故障诊断精度，已被广泛应用于AUV的故障诊断。然而，由于单一数据源提供的故障特征非常有限，这些方法在完成复杂的故障诊断任务时面临挑战。为了解决这个问题，本文提出了一种基于注意力机制的多传感器数据融合神经网络（MDFNN），用于 AUV 故障诊断。首先，引入基于具有一维内核的二维（2D）卷积方法的特征提取层，以分别从每个传感器数据中提取特征，从而显着优化模型架构。其次，提出了一种基于有效通道注意机制的特征融合层，为每个传感器数据的特征重新分配权重，使模型能够更多地关注关键特征。最后，将融合后的特征输入到全连接层和softmax层，实现多传感器数据的故障诊断。最后，利用真实的 AUV 实验数据评估了所提出的 MDFNN 的诊断性能。实验表明，所提出的MDFNN具有非常快的收敛速度和98.37%的故障诊断准确率，展示了其优异的故障诊断性能。所提出的MDFNN为具有多种类型传感器数据的AUV提供了一个通用且结构简单的故障诊断框架，具有重要的工程价值。