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

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. T 2 and S P E 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.