Networked Fault Detection of Field Equipment from Monitoring System Based on Fusing of Motion Sensing and Appearance Information

Recent development of Internet of Things (IoT) technologies has triggered a soaring application of intelligent monitoring systems. A sensitive online fault detection for industrial equipment is of utmost significance to improve industrial intelligence based on video sensor network. In this paper, we propose the Appearance and Motion SVM (AMSVM), an online fault detection method fusing multimodal information based on One-Class SVM (OCSVM), to monitor the working conditions of unsupervised equipment in the field. It utilizes multimodal features to detect faults in terms of the appearance and motion patterns of equipment. The motion pattern was generated using the OCSVM-encoded histogram of optical flow orientation (HOFO), and meanwhile we employed Local Binary Pattern Histogram (LBPH) to extract texture features to train OCSVM, depicting appearance patterns. Then, decision level information (i.e., appearance and motion patterns) are combined to produce a more precise characteristic for fault detection. The proposed method herein was validated on several industrial video surveillance data set.