湖南工业大学学报2025，Vol.39Issue(5)：16-23,8.DOI:10.3969/j.issn.1673-9833.2025.05.003
基于多模态数据融合的轴承故障诊断方法
A Bearing Fault Diagnosis Method Based on Multi-Modal Data Fusion
摘要
Abstract
In view of such flaws as insufficient precision and limited diagnostic ability under complex fault modes found in bearing fault diagnosis of traditional single mode signal processing,a bearing fault diagnosis method,which is based on convolutional neural network(CNN)and gated recurrent unit(GRU),has thus been proposed.With the time-domain and frequency-domain features of current signals and vibration signals integrated for fault classification,CNN is used for feature extraction,and GRU for capturing the long-term dependencies of time-series data,thus improving the diagnostic ability of the model.In addition,an optimization of the training process and prevent gradient vanishing or exploding can be achieved by adopting batch normalization(BN)method.Experimental results show that the proposed method is characterized with a high classification accuracy in different fault states,especially in normal operation and inner ring fault states,with its accuracy and recall rates close to 1,indicating that the proposed method possesses a strong robustness in the fault diagnosis task of multimodal data fusion.The comparative experimental results with CNN,ResNet,and MS-CNN also demonstrate that the proposed method has a higher accuracy in fault diagnosis under variable speed conditions.关键词
故障诊断/卷积神经网络/数据融合/门控循环单元/滚动轴承Key words
fault diagnosis/convolutional neural network/data fusion/gated recurrent unit/rolling bearing分类
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吴昊天,彭泽宇,刘涛,姚齐水..基于多模态数据融合的轴承故障诊断方法[J].湖南工业大学学报,2025,39(5):16-23,8.基金项目
国家自然科学基金资助项目(52305465) （52305465）
