Global-and-local-structure-based neural network for fault detection

Abstract

A novel statistical fault detection method, called the global-and-local-structure-based neural network (GLSNN), is proposed for fault detection. GLSNN is a nonlinear data-driven process monitoring technique through preserving both global and local structures of normal process data. GLSNN is characterized by adaptively training a neural network which takes both the global variance information and the local geometrical structure into consideration. GLSNN is designed to extract the meaningful low-dimensional features from original high-dimensional process data. After nonlinear feature extraction, Hotelling T² statistic and the squared prediction error (SPE) statistic are adopted for online fault detection. The merits of the proposed GLSNN method are demonstrated by both theoretical analysis and case studies on the Tennessee Eastman (TE) benchmark process. Extensive experimental results show the superiority of GLSNN in terms of missed detection rate (MDR) and false alarm rate (FAR). The source code of GLSNN can be found in https://github.com/htzhaoecust/glsnn.

Keywords: Dimension reduction; Fault detection; Feedforward neural network; Principal component analysis; Statistical process monitoring.