Rolling Bearing Fault Diagnosis Based on MTF Encoding and CBAM-LCNN Mechanism  

作　　者：Wei Liu Sen Liu Yinchao He Jiaojiao Wang Yu Gu 

机构地区：[1]School of Mechanical and Electrical Engineering,Zhoukou Normal University,Zhoukou,466001,China [2]School of Information and Control Engineering,Jilin Institute of Chemical Technology,Jilin,132022,China [3]School of Automation,Guangdong University of Petrochemical Technology,Maoming,525000,China

出　　处：《Computers, Materials & Continua》2025年第3期4863-4880,共18页计算机、材料和连续体(英文)

基　　金：supported by the National Natural Science Foundation of China(52001340);the Henan Province Science and Technology Key Research Project(242102110332);the Henan Province Teaching Reform Project(2022SYJXLX087).

摘　　要：To address the issues of slow diagnostic speed,low accuracy,and poor generalization performance in traditional rolling bearing fault diagnosis methods,we propose a rolling bearing fault diagnosis method based on Markov Transition Field(MTF)image encoding combined with a lightweight convolutional neural network that integrates a Convolutional Block Attention Module(CBAM-LCNN).Specifically,we first use the Markov Transition Field to convert the original one-dimensional vibration signals of rolling bearings into two-dimensional images.Then,we construct a lightweight convolutional neural network incorporating the convolutional attention module(CBAM-LCNN).Finally,the two-dimensional images obtained from MTF mapping are fed into the CBAM-LCNN network for image feature extraction and fault diagnosis.We validate the effectiveness of the proposed method on the bearing fault datasets from Guangdong University of Petrochemical Technology’s multi-stage centrifugal fan and Case Western Reserve University.Experimental results show that,compared to other advanced baseline methods,the proposed rolling bearing fault diagnosis method offers faster diagnostic speed and higher diagnostic accuracy.In addition,we conducted experiments on the Xi’an Jiaotong University rolling bearing dataset,achieving excellent results in bearing fault diagnosis.These results validate the strong generalization performance of the proposed method.The method presented in this paper not only effectively diagnoses faults in rolling bearings but also serves as a reference for fault diagnosis in other equipment.

关 键 词：Rolling bearing fault diagnosis markov transition field lightweight convolutional neural network convolutional block attention module 

分 类 号：TN911.7[电子电信—通信与信息系统] TP181[电子电信—信息与通信工程]