Li Ying, Ye Yuxing, Zhang Zhiwei, Wen Long
College of Power Engineering, Naval University of Engineering, No.177 Jiefang Road, Wuhan 430033, China.
School of Mechanical Engineering and Electronic Information, China University of Geosciences, No. 388 Lumo Road, Wuhan 430074, China.
Sensors (Basel). 2025 Jul 22;25(15):4539. doi: 10.3390/s25154539.
Autonomous Underwater Vehicles (AUVs) are gradually becoming some of the most important equipment in deep-sea exploration. However, in the dynamic nature of the deep-sea environment, any unplanned fault of AUVs would cause serious accidents. Traditional fault diagnosis models are trained in static and fixed datasets, making them difficult to adopt in new and unknown deep-sea environments. To address these issues, this study explores incremental learning to enable AUVs to continuously adapt to new fault scenarios while preserving previously learned diagnostic knowledge, named the RM-MFKAN model. First, the approach begins by employing the Rainbow Memory (RM) framework to analyze data characteristics and temporal sequences, thereby delineating boundaries between old and new tasks. Second, the model evaluates data importance to select and store key samples encapsulating critical information from prior tasks. Third, the RM is combined with the enhanced KAN network, and the stored samples are then combined with new task training data and fed into a multi-branch feature fusion neural network. The proposed RM-MFKAN model was conducted on the "Haizhe" dataset, and the experimental results have demonstrated that the proposed model achieves superior performance in fault diagnosis for AUVs.
自主水下航行器（AUV）正逐渐成为深海勘探中一些最重要的设备。然而，在深海环境的动态特性下，AUV的任何意外故障都可能导致严重事故。传统的故障诊断模型是在静态和固定的数据集中进行训练的，这使得它们难以应用于新的和未知的深海环境。为了解决这些问题，本研究探索增量学习，以使AUV能够在保留先前学到的诊断知识的同时，不断适应新的故障场景，即RM-MFKAN模型。首先，该方法通过采用彩虹记忆（RM）框架来分析数据特征和时间序列，从而划分新旧任务之间的边界。其次，该模型评估数据重要性，以选择和存储封装先前任务关键信息的关键样本。第三，将RM与增强型KAN网络相结合，然后将存储的样本与新任务训练数据相结合，并输入到多分支特征融合神经网络中。所提出的RM-MFKAN模型在“海蜇”数据集上进行了测试，实验结果表明，该模型在AUV故障诊断中具有卓越的性能。
