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

作　　者：吴昊天 彭泽宇 刘涛 姚齐水[1,2] WU Haotian;PENG Zeyu;LIU Tao;YAO Qishui(School of Mechanical Engineering,Hunan University of Technology,Zhuzhou Hunan 412007,China;High Performance Rolling Bearing Technology,Key Laboratory of Hunan Provincial Universities,Zhuzhou Hunan 412007,China)

机构地区：[1]湖南工业大学机械工程学院,湖南株洲412007 [2]高性能滚动轴承技术湖南省高校重点实验室,湖南株洲412007

出　　处：《湖南工业大学学报》2025年第5期16-23,共8页Journal of Hunan University of Technology

基　　金：国家自然科学基金资助项目(52305465)。

摘　　要：针对传统单一模态信号处理在轴承故障诊断中面临的精度不足和复杂故障模式下诊断能力有限的问题,提出了一种基于卷积神经网络(CNN)和门控循环单元(GRU)的轴承故障诊断方法。通过融合电流信号和振动信号的时域与频域特征进行故障分类,采用CNN进行特征提取,并利用GRU捕捉时序数据的长期依赖关系,以提高模型的诊断能力。此外,采用批量归一化(BN)方法优化训练过程,防止梯度消失或爆炸。实验结果表明:所提方法在不同故障状态下具有较高的分类精度,尤其在正常运行和内圈故障状态下,精度和召回率均接近1,表明该方法在多模态数据融合的故障诊断任务中具有较强的鲁棒性;与CNN、ResNet、MS-CNN的对比实验结果,也证明了本文所提方法在变转速工况的故障诊断中具有更高的准确性。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 

分 类 号：TH133[机械工程—机械制造及自动化] TP18[自动化与计算机技术—控制理论与控制工程]