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.

如果AUV要在没有人工帮助的复杂环境中执行任务，则必须能够进行故障诊断。但是，当前的水下机器人故障诊断方法缺乏人工经验和准确性，导致故障处理能力不足。与传统的基于模型的故障诊断不同，我们提出了一种新的无模型故障诊断方法，其特征在于基于深度学习的算法，这是一种新的序列卷积神经网络（SeqCNN），用于学习状态数据和故障类型之间的模式。更具体地说，提出的SeqCNN旨在从状态数据中提取全局特征和局部特征，并将提取的信息分类为不同的特征。故障类型，并且可以将两阶段诊断模式转换为单阶段诊断模式。与传统的基于模型的诊断相比，它可以显着减少耗时的负担，简化诊断过程并提高效率。SeqCNN的有效性已通过在小型四旋翼AUV“海哲”上的实际实验得到验证。结果表明，所提出的SeqCNN可以解决单级诊断模式下的故障检测和故障隔离问题，其准确性远远优于其他深度学习诊断算法。