文献类型：期刊文献

英文题名：Rotating machinery structural faults feature enhancement and diagnosis base on low-pass Teager energy operator intrinsic time-scale decomposition

作者：Song, Xuewei[1,2,3];Huang, Zhende[1];Liang, Guanlong[1];Niu, Jinzhang[1];Jia, Baozhu[1,2,3];Liao, Zhiqiang[1,2,3]

机构：[1]Guangdong Ocean Univ, Naval Architecture & Shipping Coll, Zhanjiang 524088, Peoples R China;[2]Tech Res Ctr Ship Intelligence & Safety Engn Guang, Zhanjiang 524088, Peoples R China;[3]Guangdong Prov Key Lab Intelligent Equipment South, Zhanjiang 524088, Peoples R China

年份：2025

卷号：36

期号：3

外文期刊名：MEASUREMENT SCIENCE AND TECHNOLOGY

收录：SCI-EXPANDED(收录号：WOS:001421910600001)、、EI(收录号：20250817907774)、Scopus(收录号：2-s2.0-85217951736)、WOS

基金：The work is supported by the National Natural Science Foundation of China (Grant Nos. 52401418, 52201355 and 52071090), the Program for Scientific Research Start-Up Funds of Guangdong Ocean University (Grant Nos. 060302132304 and 060302132101) and Zhanjiang Non-Funded Science and Technology Research Project (Grant Nos. 2023B01046 and 2022B01049). The authors thank the postgraduate students for helping them collect the dataset. The valuable comments and suggestions from the reviewers are very much appreciated.

语种：英文

外文关键词：rotating machinery; structural faults; low-pass Teager energy operator intrinsic time-scale decomposition (LTEO-ITD); fault diagnosis

外文摘要：Aiming to address the issue of the complex and harsh working environment of rotating machinery, the features of vibration signals associated with structural faults are often obscured by noise, resulting in low accuracy in fault diagnosis. This paper proposes a method for feature enhancement and diagnosis of rotating machinery structural faults, which combines the low-pass Teager energy operator intrinsic time-scale decomposition (LTEO-ITD) recurrence plot (RP) with the ResNet18 network. Firstly, the low-frequency components of the vibration signal are extracted and enhanced using the LTEO. The method effectively suppresses noise interference and enhances fault features. Then, the fault features are extracted using ITD. The component that contains the highest number of fault features is selected based on kurtosis analysis, followed by the generation of the corresponding RP. Finally, the data is input into the ResNet18 network for diagnostic verification. The effectiveness and feasibility of the proposed method are verified through vibration signals from the rotating machinery experimental platform and the comprehensive rotating machinery experimental platform. The proposed method achieves a diagnostic accuracy of 100% on both datasets. The comparative validation was conducted using five distinct image encoding methods. The experimental results show that the proposed method effectively extracts fault features of structural faults, thereby enhancing the accuracy of fault diagnosis.