基于多尺度时间卷积网络的多模态过程故障诊断方法

FAULT DIAGNOSIS BASED ON MULTISCALE TEMPORAL CONVOLUTIONAL NETWORK FOR MULTIMODE INDUSTRIAL PROCESS

针对工业过程故障诊断面临的多模态、多尺度等混合特性问题,提出一种基于多尺度时间卷积网络的故障诊断方法。考虑到过程数据的多模态分布特性,采用基于余弦相似度的局部近邻标准化方法处理过程数据以消除多模态特性;针对过程数据的多尺度特性,使用变分模态分解获取数据的多尺度表示,对各分量构建采用注意力机制的时间卷积网络模型提取特征,并融合多尺度特征,以实现多尺度特征提取;在特征提取的基础上使用全连接层实现故障诊断。通过田纳西-伊斯曼(Tennessee-Eastman,TE)过程仿真实验验证了该方法的有效性和可行性。

Aimed at the problem of industrial process fault diagnosis with the mixed characteristics of multimode and multiscale,a fault diagnosis method based on multiscale temporal convolutional network is proposed.Considering the multimode distribution characteristics of process data,we used the local neighborhood standardization method based on cosine similarity to standardize the process data to eliminate the multimode characteristics.Aimed at the multiscale characteristics of the process data,the multiscale representation of the process data was obtained by variational mode decomposition,a temporal convolutional network model with attention mechanism was constructed for each component to extract features,and the multiscale features were fused to achieve multiscale feature extraction.On the basis of the feature extraction,the fault diagnosis was realized by a full connection layer.The effectiveness and feasibility of the proposed method are verified by Tennessee-Eastman(TE)process simulation experiments.