文献类型：期刊文献

英文题名：Bearing-Fault-Feature Enhancement and Diagnosis Based on Coarse-Grained Lattice Features

作者：Li, Xiaoyu[1];Jia, Baozhu[1,2,3];Liao, Zhiqiang[1,2,3];Wang, Xin[1]

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 Ocean Univ, Guangdong Prov Key Lab Intelligent Equipment South, Zhanjiang 524088, Peoples R China

年份：2024

卷号：24

期号：11

外文期刊名：SENSORS

收录：SCI-EXPANDED(收录号：WOS:001245267900001)、、EI(收录号：20242516265311)、Scopus(收录号：2-s2.0-85195859965)、WOS

基金：No Statement Available

语种：英文

外文关键词：rolling bearings; fault diagnosis; coarse-grained lattice features; Swin Transformer; feature enhancement

外文摘要：In view of the frequent failures occurring in rolling bearings, the strong background noise present in signals, weak features, and difficulties associated with extracting fault characteristics, a method of enhancing and diagnosing rolling bearing faults based on coarse-grained lattice features (CGLFs) is proposed. First, the vibrational signals of bearings are subjected to adaptive filtering to eliminate background noise. Second, frequency-domain transformation is performed, and a coarse-grained approach is used to continuously segment the spectrum. Within each segment, amplitude-enhancement operations are executed, transforming the data into a CGLF graph that enhances fault characteristics. This graph is then fed into a Swin Transformer-based pattern-recognition network. Third and finally, a high-precision fault diagnosis model is constructed using fully connected layers and Softmax, enabling the diagnosis of bearing faults. The fault recognition accuracy reaches 98.30% and 98.50% with public datasets and laboratory data, respectively, thereby validating the feasibility and effectiveness of the proposed method. This research offers an efficient and feasible fault diagnosis approach for rolling bearings.