Segmentation Improved Label Propagation for Semi-Supervised Anomaly Detection in Fused Magnesia Furnace Process

Abstract

This article introduces a semi-supervised solution to the practical task of identifying the semi-molten working condition for fused magnesium furnace. The classifier is trained under the semi-supervised learning framework using video data which are partially labeled, and it works on-line to classify the semi-molten working condition from the monitoring video of fused magnesium furnace. Firstly, a deep auto-encoder is used to extract features from the video. Next, the feature time series is segmented into subsequence through a bottom-up algorithm which maximizes the interior data correlation. Then, a graph based label propagation algorithm is employed to iteratively train a LSTM classifier using the subsequences as training samples and improve the accuracy of the LSTM classifier. The main contribution of this work is the novel segmentation algorithm for sequential data which can remarkably improve the classification accuracy. The advantages of the proposed method are demonstrated by experiments and comparative analysis based on industrial data.

Keywords

• Semi-supervised classification
• long short-term memory
• time series segmentation
• fused magnesium furnace
• anomaly detection
• graph based label propagation