Industrial process fault detection based on KGLPP model with Cam weighted distance

Abstract

The nearest neighbor selection of multivariate statistical projection analysis methods assumes locally constant probabilities. However, ignoring the non-uniform distributed characteristic of data causes information redundancy in data-intensive regions and insufficient information in data-sparse regions, leading to detection performance decline. In this study, a new weighted distance named Cam weighted distance is used to reselect the neighbors and consequently overcome the aforementioned limitation. An nonlinear industrial fault detection method based on KGLPP-Cam is developed. The proposed method can preserve not only global and local information but also orientation and adaptive scale to obtain the information of neighbors according to different surroundings. T2 and SPE statistics are calculated for fault detection. A change ratio function is constructed to select sensitive principal components adaptively and better describe the sensitivity of different projection directions for processing change information. The proposed method is examined through a numerical example and TE process.