关键词: 间歇过程, 故障诊断, 扩散距离, 邻域保持嵌入

Abstract: Traditionally, Euclidean distance determined only by neighborhood preserving embedding (NPE) algorithm is usually adopted to select the nearest neighbors for attracting characteristic features in fault diagnosis in industries. The troubleshooting effects in this way for the fault diagnosis are not effectively enough as well known. By employing a diffusion distance (DD) and combining it with NPE algorithm, a new fault diagnosis method based on the diffusion distance neighborhood preserving embedding (DDNPE) algorithm is proposed in this article by the author. The proposed new method explores the inherent popular structure embedded in the original high-dimensional data first, and reduces the data dimension, then extracts the characteristic information from the original data by learning the potential geometry information contained in the original data, while keeping local information structure in the original data popularity and unchanged. By doing so, it avoids the existing problem that the NPE algorithm selects the characteristics of the neighborhoods only by Euclidean distance. The problem of insufficient feature extraction can be avoided. It is now becoming possible to detect faults in fault diagnosis in industries by using T² and SPE statistics and the fault variable can be also diagnosed by the variable contribution graph method. Simulation results of penicillin fermentation process coming out from this research demonstrate that the proposed method is effective indeed.

Key words: batch process, fault diagnosis, diffusion distance, neighborhood preserving embedding