基于KECA和FWA-SVM的间歇过程分时段故障诊断方法 postprint

Abstract: Aiming at the high complexity, strong nonlinearity and strong time characteristics of intermittent process, this paper proposed a new method based on kernel entropy component analysis (KECA) to reduce the dimensionality of the KECA characteristic variables, and used the fireworks algorithm (FWA) to optimize the support vector machine (SVM) parameters for the intermittent process of division fault diagnosis method. Firstly, it carried out multi-directional kernel principal component analysis (MKPCA) for the on-line fault monitoring and output the fault data. Second, it used K-means method to divid the batch process into several sub-periods. It used KECA to reduce characteristic variable dimensionality according to the contribution rate of entropy to determine the number of selected elements and extracted feature information in depth. Finally, constructed FWA optimized SVM parameter fault diagnosis model in each sub-period, put the reduced dimension processed fault data into their own sub-period FWA-SVM diagnostic model for fault diagnosis. Through a variety of comparative experimental study based on penicillin simulation data, verified the feasibility and effectiveness of this method.