董林峰,马洁

• 1:北京信息科技大学机电工程学院

摘要(Abstract)：

针对轴承复合故障诊断中存在噪声干扰严重、信噪比低、复合故障特征耦合性强等问题，提出一种融合自适应噪声完备集合经验模态分解（complete ensemble empirical mode decomposition with adaptive noise, CEEMDAN）、小波能量特征以及时空注意力卷积神经网络（spatiotemporal attention convolutional neural network, STACNN）的轴承故障诊断方法。将CEEMDAN对振动信号的自适应分解优势与小波能量特征提取相结合，更全面、精准地提取轴承故障相关特征；通过构建包含空间卷积模块、时空注意力门控机制以及时间注意力门控循环单元（gated recurrent unit, GRU）的神经网络模型，有效提升了模型对时空特征的学习和表达能力；利用智能数据增强技术扩充训练数据的多样性，同时借助自适应参数优化策略，寻找最优的模型参数组合，提高模型诊断性能。实验结果表明：该方法在强噪声干扰下诊断准确率达98.92%；与传统特征提取、神经网络和注意力机制等方法相比，准确率明显提升，证明了该方法在轴承故障诊断中的有效性和优越性。

关键词(KeyWords)： 自适应噪声完备集合经验模态分解;时空注意力卷积神经网络;复合故障诊断;小波能量特征

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金项目（61973041）

作者(Author): 董林峰,马洁

DOI: 10.16508/j.cnki.11-5866/n.2025.05.008

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