文献类型：期刊文献

英文题名：A Physics-Guided Wavelet Feature Extraction Method for Fault Diagnosis of Thruster Blades

作者：Xie, Tao[1,2];Hu, Zhihuan[1];Zhang, Weidong[1];Li, Zhongmei[3];Chen, Hongtian[1]

机构：[1]Shanghai Jiao Tong Univ, Dept Automat, Shanghai 200240, Peoples R China;[2]Hubei Engn Res Ctr Intelligent Detect & Identifica, Wuhan 430200, Peoples R China;[3]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份：2024

卷号：73

外文期刊名：IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT

收录：;EI(收录号：20243616981470);WOS:【SCI-EXPANDED(收录号:WOS:001308263200011)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 62303305 and Grant U2141234,in part by the Shanghai Pujiang Program under Grant 23PJ1404700, in part by the National Key Research and Development Program of China under Grant 2022ZD0119900, in part by the Shanghai Science and Technology Program under Grant 22015810300, in part by the Hainan Province Science and Technology Special Fund under Grant ZDYF2021GXJS041, in part by the Open Projects funded by Hubei Engineering Research Center for Intelligent Detection and Identification of Complex Parts under Grant IDICP-KF-2024-09, and in part by the Open Research Project of the State Key Laboratory of Industrial Control Technology under Grant ICT2024B15.

语种：英文

外文关键词：physical feature; thruster; unmanned surface vehicles (USVs); wavelet feature extraction; physical feature; thruster; Fault diagnosis; Fault diagnosis; wavelet feature extraction

摘要：Unmanned surface vehicles (USVs) are more environmentally friendly than traditional vessels, reducing emissions and minimizing their impact on aquatic ecosystems. In this article, a thruster fault diagnosis method based on physics-guided wavelet features (PGWFs) is proposed, specifically for application in USVs. First, the transformation coefficient components are computed through wavelet decomposition. Next, the physical components undergo a reconstruction process to obtain segmented transformation signals. Finally, fault-relevant features are extracted and trained using a convolutional neural network (CNN), while the unlabeled features are used to test the network for thruster fault diagnosis. An experimental platform based on a USV prototype confirmed the validity of this method, demonstrating its superiority over existing methods.