基于时序图像深度学习的电熔镁炉异常工况诊断

Ultra-high temperature smelting process of fused magnesium furnace (FMF) is difficult to monitor and prone to the semi-molten abnormal condition, which exerts severe influences not only on product quality but also on security of production and workers. Traditionally, the practioners have to reach out for inspection by watching the furnace shell. This is difficult in real-time and can cause missed alarms, wrong alarms, even lead to melting through the iron shell of the furnace. To solve this problem, the paper uses the information from in-situ video and takes advantage of the spatial-temporal features of the semi-molten abnormal condition, namely, the spatial feature appearing as irregular highlighted regions on the local furnace shell and the temporal feature appearing as increasing brightness and increasing areas of those regions. Based on the spatial-temporal features, this paper proposes a novel convolutional recurrent neural network (CRNN)-based method for the abnormal condition diagnosis of FMF. The method is composed of three modules: a preprocessing of the original video, a convolutional neural network (CNN)-based spatial feature extraction and a recurrent neural network (RNN)-based temporal feature extraction and a weighted median filter-based automatic labeling algorithm. By applying videos from the furnace shell of a real FMF, the proposed method is tested and compared with other two deep learning-based baseline approaches, and the results demonstrate the superiority of the proposed method.