文献类型：期刊文献

英文题名：Bearing Fault Feature Enhancement and Diagnosis Based on Savitzky-Golay Filtering Gramian Angular Field

作者：Huang, Zhende[1];Song, Xuewei[2];Liao, Zhiqiang[1];Jia, Baozhu[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; Guangdong Prov Key Lab Intelligent Equipment South, Zhanjiang 524088, Peoples R China

年份：2024

卷号：12

起止页码：87991

外文期刊名：IEEE ACCESS

收录：SCI-EXPANDED(收录号：WOS:001258782600001)、、EI(收录号：20242716608029)、Scopus(收录号：2-s2.0-85197094596)、WOS

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 52201355 and Grant 52071090, in part by the Program for Scientific Research Start-Up Funds of Guangdong Ocean University under Grant 060302132304 and Grant 060302132101, and in part by Zhanjiang Non-Funded Science and Fechnology Research Project under Grant 2022B01049 and Grant 2023B01046.

语种：英文

外文关键词：Feature extraction; Information filters; Low-pass filters; Accuracy; Smoothing methods; Vibrations; Time series analysis; Fault diagnosis; Residual neural networks; Bearings fault diagnosis; Savitzky-Golay filtering; Gramian angle field; fault feature enhancement; residual network

外文摘要：In actual engineering production, bearings typically operate in harsh environments. The fault features of bearing vibration signals are often submerged by background noise, making it difficult to extract the fault signal features and impacting the accuracy of fault diagnosis. To address this problem, this paper proposes a bearing fault diagnosis method based on the Savitzky-Golay Gramian Angular Field (GAF) with fault feature enhancement combined with ResNet. First, the acquired vibration signals are segmented, and the segmented signals are subjected to Butterworth high-pass filtering to obtain the high-frequency components of the signals that contain fault information. Secondly, the extracted high-frequency components are boosted by the S-enhancement algorithm for fault features. The boosted signals are then filtered by Savitzky-Golay to achieve data smoothing aggregation enhancement. Subsequently, the feature-enhanced GAF graphs are obtained using the transformation method. Finally, bearing fault diagnosis is performed using the Glamian Angle field diagram as input to the ResNet18 model. To verify the feasibility of the proposed method, experiments were conducted using Case Western Reserve University (CWRU) bearing fault dataset and bearing fault dataset of laboratory experimental platform. The experimental results showed that the fault diagnosis accuracy were 99.28% and 100%, respectively. The results validated the feasibility of the proposed method. Through comparative experiments with the Symmetric Dot Pattern (SDP) method, the traditional GAF method and the Recurrence Plots (RP) method, the results demonstrate that the proposed method has high diagnostic accuracy, proved the effectiveness of the method.