Notice of Violation of IEEE Publication Principles: Fault Detection Using Industrial Video Information

Abstract

Notice of Violation of IEEE Publication Principles Fault Detection Using Industrial Video Information by Y. Zhang, Z. Wang, Y. Fu and X. Li, in the IEEE Transactions on Industrial Electronics, vol. PP, no. 99 After careful and considered review of the content of this paper and associated multimedia content by a duly constituted expert committee, this work has been found to be in violation of IEEE's Publication Principles. This paper and associated multimedia files contain fraudulent data and images. Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper and refrain from future references. In this study, a new data-modelling algorithm, referred to as relaxed independent component analysis, is proposed for the purpose of industrial video process monitoring. The contributions are as follows. (1) Physical variables and image data are unified in a framework to propose fault detection method of industrial process for the first time. (2) We find that independent components are sensitive to extract features of image and physical variables. In original KICA method, KPCA+ICA are used to extract nonlinear independent components. Since ICA is linear method, the original method only extracts semi-nonlinear independent components. In our method, the complete nonlinear independent components are extracted using the independence criterion. (3) A process monitoring approach of relaxed independent component analysis is presented. When applied to the monitoring of an electrical fused magnesia furnace, it can effectively improve the accuracy of forecasting the non-Gaussian process and reduce the ratio of false alarms compared to kernel principle component analysis and traditional kernel independent component analysis.