基于注意力机制与多源信息融合的变工况轴承故障诊断

Attention mechanism and multi-source information fusion-based method for bearing fault diagnosis under variable operating conditions

针对轴承在变工况下工作时受环境噪声和工况变化的干扰,现有的基于单源信号的轴承故障诊断方法因单源信号难以提供全面且稳定的故障信息,导致诊断效果不理想的问题,提出一种基于注意力机制的多源信息融合网络模型(MSIFNM)。该模型的多尺度特征提取模块可以提取更充足的故障特征信息;双阶段注意力模块从多个维度增强对工况变化不敏感的故障特征;多源信息特征加权模块根据不同传感器信号对不同故障的敏感程度,自适应地对多源信息进行权重分配;特征融合与类别输出模块实现对加权后的特征进一步融合与特征提取,再经全连接层和Softmax层输出分类结果。采用变转速和变负载轴承故障数据集对本文所提的MSIFNM模型进行实验验证,实验结果表明,MSIFNM可以有效实现多源信息融合特征提取,提高变工况条件下轴承故障诊断的准确性、稳定性和工况自适应性。

When bearings operate under variable conditions,they are affected by environmental noise and fluctuations in operating parameters.Existing bearing fault diagnosis methods relying on single-source signals struggle in these situations because such signals often fail to provide comprehensive and stable fault information.To address this issue,this paper proposes a multi-source information fusion network model(MSIFNM)based on an attention mechanism.The model′s multi-scale feature extraction module captures more detailed fault features,while the two-stage attention module enhances features that are less sensitive to changes in operating conditions.The multi-source information feature weighting module adaptively assigns weights to the features based on their sensitivity to faults.The feature fusion and classification output module further integrates these weighted features and delivers classification results through fully connected and softmax layers.To validate the effectiveness of the proposed MSIFNM model,bearing datasets under variable speed and load conditions were used.Experimental results demonstrate that the MSIFNM model significantly improves the accuracy,stability,and adaptability of bearing fault diagnosis under variable operating conditions.