A unified representation and fusion framework of multi-source heterogeneous data for fault diagnosis in industrial processes

In the increasingly integrated and complex industrial processes, it is difficult for a single type of data to adequately characterize faults due to the correlation and multi-source heterogeneity of process data. Therefore, to achieve accurate fault diagnosis, multi-source heterogeneous data is necessary to be integrated to obtain comprehensive and multi-scale fault information. However, significant differences in dimensions and structures are exhibited by multi-source heterogeneous data, which may make the high-quality data fusion difficult to achieve. Responding to the above issues, in order to achieve a unified representation and fusion of multi-source heterogeneous data, and enhance the fault diagnosis performance, a unified representation and fusion framework is proposed for multi-source heterogeneous data based fault diagnosis in industrial processes. Specifically, the optimal region containing useful information in the fault image data is first selected as the representative region. Subsequently, tensor fusion is performed on the time series data and the selected region of image data respectively, and the low-rank decomposition is used to obtain the low-dimensional unified vector representation for data fusion. Furthermore, a fault diagnosis module based on the multi-scale lightweight convolutional neural network is constructed in which the multi-scale features are extracted and fused from the fusion vector and then used for fault diagnosis. Finally, to validate the effectiveness and superiority of the proposed framework, comparative experiments based on actual data from the hot rolling process are conducted.