文献类型：期刊文献

英文题名：Optimal Time Frequency Fusion Symmetric Dot Pattern Bearing Fault Feature Enhancement and Diagnosis

作者：Liang, Guanlong[1,2,3];Song, Xuewei[1,2,3];Liao, Zhiqiang[1,2,3];Jia, Baozhu[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：2024

卷号：24

期号：13

外文期刊名：SENSORS

收录：SCI-EXPANDED(收录号：WOS:001269756500001)、、EI(收录号：20242916704981)、Scopus(收录号：2-s2.0-85198339205)、WOS

基金：This research was funded by the National Natural Science Foundation of China (52201355, 52071090), the Program for Scientific Research Start-Up Funds of Guangdong Ocean University(060302132304, 060302132101), and the Zhanjiang Non-funded Science and Technology Research Project (2022B01049, 2023B01046).

语种：英文

外文关键词：bearing fault diagnosis; signal feature enhancement; optimal time frequency fusion SDP; deep convolutional neural network (DCNN)

外文摘要：Regarding the difficulty of extracting the acquired fault signal features of bearings from a strong background noise vibration signal, coupled with the fact that one-dimensional (1D) signals provide limited fault information, an optimal time frequency fusion symmetric dot pattern (SDP) bearing fault feature enhancement and diagnosis method is proposed. Firstly, the vibration signals are transformed into two-dimensional (2D) features by the time frequency fusion algorithm SDP, which can multi-scale analyze the fluctuations of signals at minor scales, as well as enhance bearing fault features. Secondly, the bat algorithm is employed to optimize the SDP parameters adaptively. It can effectively improve the distinctions between various types of faults. Finally, the fault diagnosis model can be constructed by a deep convolutional neural network (DCNN). To validate the effectiveness of the proposed method, Case Western Reserve University's (CWRU) bearing fault dataset and bearing fault dataset laboratory experimental platform were used. The experimental results illustrate that the fault diagnosis accuracy of the proposed method is 100%, which proves the feasibility and effectiveness of the proposed method. By comparing with other 2D transformer methods, the experimental results illustrate that the proposed method achieves the highest accuracy in bearing fault diagnosis. It validated the superiority of the proposed methodology.