噪声与振动控制2025，Vol.45Issue(2)：82-89,8.DOI:10.3969/j.issn.1006-1355.2025.02.014
基于AMCNN-BiLSTM-CatBoost的滚动轴承故障诊断模型研究
Fault Diagnosis Model of Rolling Bearings Based on AMCNN-BiLSTM-CatBoost
摘要
Abstract
Aiming at the problems of poor classification accuracy and low operational efficiency of existing bearing fault diagnosis models,a rolling bearing fault diagnosis model based on attention mechanism,convolutional neural network(AMCNN),bidirectional long short-term memory(BiLSTM)network and CatBoost was proposed.Firstly,the original vibra-tion signal was processed by down-sampling technique,and then the down-sampled vibration signal was used as the model input to extract features through three different convolution modules.Then,the channel attention module was used to carry out weighted fusion of the extracted features,and then the weighted fusion data was input into the BiLSTM network to fur-ther extract the timing feature information.Finally,the timing feature information was input into CatBoost for fault classifica-tion.Experiments show that this model can not only guarantee the high accuracy of fault diagnosis,but also greatly shorten the training time of the network.关键词
故障诊断/卷积神经网络/双向长短期记忆网络/注意力机制/CatBoost/轴承Key words
fault diagnosis/convolutional neural network/bidirectional long short-term memory network/attention mechanism/CatBoost/bearing分类
机械制造引用本文复制引用
袁建华,邵星,王翠香,皋军..基于AMCNN-BiLSTM-CatBoost的滚动轴承故障诊断模型研究[J].噪声与振动控制,2025,45(2):82-89,8.基金项目
国家自然科学基金资助项目(62076215) （62076215）
教育部新一代信息技术创新资助项目(2020ITA02057) （2020ITA02057）
盐城工学院研究生科研与实践创新计划资助项目(SJCX22_XZ035、SJCX22_XY061) （SJCX22_XZ035、SJCX22_XY061）
