A novel multiple temporal-spatial convolution network for anode current signals classification

Anode current signals (ACS) play an important role in aluminum reduction production. Owing to the complexity dynamic and temporal-spatial dependency characteristics, classification of ACS is a challenging problem and the existing classification methods are failed to capture these characteristics. To address this issue, a multiple temporal-spatial convolution network (MTSCN) combining graph convolutional network (GCN) and one-dimension convolutional neural network (1-D-CNN) is proposed in this paper. Firstly, a adjacency matrix is first introduced to characterize spatial structure of ACS. Secondly, based on the spatial structure, a novel machine learning framework which combines GCN and 1-D-CNN is proposed. Specifically, multi-layer of 1-D-CNN and multi-layer of GCN are used to capture temporal and spatial dependencies of ACS, respectively. The obtained data-dirved model is able to identify abnormalities of ACS. Finally, results carried out in real-world ACS data set are given to verify the effectiveness of the proposed method.