Disturbance Robust Abnormality Diagnosis of Fused Magnesium Furnaces Using Deep Neural Networks

Abstract

Operation abnormalities of fused magnesium furnaces (FMFs), e.g., semi-molten, can degrade the product quality and operation performance. The abnormalities can even lead to accidents caused by leakage of the fusing fluids with ultra-high temperatures. Therefore, it is essential to identify the semi-molten abnormality timely and accurately. In view of the spatiotemporal characteristics of the image sequences of the furnace shell under abnormal conditions of the FMFs, and the existence of strong disturbances caused by water mist, white spot, and flame fluctuation on top of the furnace, this paper establishes a novel deep learning architecture for operation abnormality diagnosis with robustness to disturbances of the FMFs. The new scheme is composed of two parts, i.e., a predictive neural networks (PredNet) for disturbance processing, and a deep three-dimensional convolutional recurrent neural networks (3DCRNN) for abnormality diagnosis. First, PredNet-based unsupervised learning is incorporated with image residual extraction for disturbance processing. Second, using the clean image sequences after disturbance processing, a new deep 3DCRNN that integrates three-dimensional CNN (3DCNN) and long-short term memory (LSTM) is proposed for enhanced spatiotemporal feature extraction and semi-molten abnormality diagnosis. The 3DCRNN successfully overcomes the limitation of conventional 3DCNN that focuses on local spatiotemporal extraction and loses the opportunities to capture long-term changes. The experimental results using the image sequences collected from a real FMF demonstrate the effectiveness of the proposed method.