Online Monitoring Scheme Using GLPP Through Kantorovich Distance Combined With a Sliding Window Technique for Nonlinear Dynamic Process Fault Detection

Abstract

To address the issue of insufficient fault detection performance of global–local preserving projections (GLPP) in the detection of minor faults within nonlinear dynamic processes, a novel fault detection method based on GLPP and Kantorovich distance combined with a sliding window (GLPP-KD) is proposed. Firstly, the GLPP algorithm is used to construct a weight matrix to retain the key information of the data, and the objective function containing local and global information is transformed into a generalized eigenvector problem to obtain a projection matrix. Additionally, the sliding window technique integrated with the Kantorovich distance is employed to quantify the discrepancies between probability distributions, thereby capturing the local dynamic characteristics of the data. Eventually, the fault detection task is achieved by identifying the minor distinctions between normal and faulty states. Experimental results show that compared with traditional methods, GLPP-KD improves the fault detection accuracy and effectively reduces the false alarm rate. The proposed method provides a strong guarantee for the safe and stable operation of the industry and has high application value.