Affiliation:
1. School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
Abstract
Autonomous underwater vehicles (AUVs) are an important equipment for ocean investigation. Actuator fault diagnosis is essential to ensure the sailing safety of AUVs. However, the lack of failure data for training due to unknown ocean environments and unpredictable failure occurrences is challenging for fault diagnosis. In this paper, a meta-self-attention multi-scale convolution neural network (MSAMS–CNN) is proposed for the actuator fault diagnosis of AUVs. Specifically, a two-dimensional spectrogram of the vibration signals obtained by a vibration sensor is used as the neural network’s inputs. The diagnostic model is fitted by executing a subtask-based gradient optimization procedure to generate more general degradation knowledge. A self-attentive multi-scale feature extraction approach is used to utilize both global and local features for learning important parameters autonomously. In addition, a meta-learning method is utilized to train the diagnostic model without a large amount of labeled data, which enhances the generalization ability and allows for cross-task training. Experimental studies with real AUV data collected by vibration sensors are conducted to validate the effectiveness of the MSAMS–CNN. The results show that the proposed method can diagnose the rudder and thruster faults of AUVs in the cases of few-shot diagnosis.
Funder
National Key Research and Development Program
Fundamental Research Funds for the Central Universities
National Natural Science Foundation of China
National Basic Scientific Research Program
Subject
Ocean Engineering,Water Science and Technology,Civil and Structural Engineering
Reference31 articles.
1. Fuzzy torque trajectory control of a rotary series elastic actuator with nonlinear friction compensation;Fotuhi;ISA Trans.,2021
2. Fault diagnosis method of autonomous underwater vehicle based on deep learning;Sun;IOP Conf. Ser. Mater. Sci. Eng.,2021
3. Deep reinforcement learning for vectored thruster autonomous underwater vehicle control;Liu;Complexity,2021
4. Tsai, C.M., Wang, C.S., Chung, Y.J., Sun, Y.D., and Perng, J.W. (2021). Multi-Sensor Fault Diagnosis of Underwater Thruster Propeller Based on Deep Learning. Sensors, 21.
5. The title of the cited article;Chu;Proc. Inst. Mech. Eng. Part M: J. Eng. Marit. Environ.,2022
Cited by
6 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献