关键词: 故障监控, 核函数全影结构投影, 统计量模式分析

Abstract:

Total kernel projection to latent structures (T-KPLS) has been widely used in the fault detection control field, its core idea is to conduct the covariance matrix decomposition of the data matrix, without using the higher-order statistics and other useful information of the data, which will cause an information loss in the feature extraction process, then result in a bad fault recognition performance. Aiming to solve the problem, a statistics pattern analysis (SPA) combing with the T-KPLS based multi-way statistics pattern analysis total kernel projection to latent structures (MSPAT-KPLS) is proposed. First, different order statistics of the data samples are constructed to map the data from the original data space into the statistic sample space, then utilize kernel function to map the statistic sample space into the higher dimensional kernel space, and according to the quality variable, the feature space will be divided into 4 subspaces, namely: process variable related to quality variable space, process variable not related to quality variable space, process variable orthogonal to quality variable space and residual error space; Lastly, aiming at the process variable related to quality variable subspace and the residual error space, different detection models are constructed, which will trace the fault variables when faults are detected. In the end, apply the proposed method on the microbial fermentation process, and the comparison results with the traditional methods show that the proposed method could achieve a better detection.

Key words: fault monitoring, total kernel projection to latent structrues, statistics pattern analysis