Phase partition and identification based on kernel entropy component analysis and multi-class support vector machines-fireworks algorithm for multi-phase batch process fault diagnosis. (August 2020)

• Record Type: Journal Article
• Authors: Zhang, Min; Wang, Ruiqi; Cai, Zhenyu; Cheng, Wenming
• Is Part Of: Transactions of the Institute of Measurement and Control. Volume 42:Number 12(2020)
• Page Start: 2324
• Page End: 2337
• Publication Date: 2020-08
• Subjects: Batch process -- multi-phase fault diagnosis -- kernel entropy component analysis -- fireworks algorithm -- multi-class support vector machine; Automatic control -- Periodicals; Measuring instruments -- Periodicals; Commande automatique -- Périodiques; Mesure -- Instruments -- Périodiques; 681.2
• DOI: 10.1177/0142331220910885
• Languages: English
• ISSNs: 0142-3312

Abstract

For the characteristics of nonlinear and multi-phase in the batch process, a self-adaptive multi-phase batch process fault diagnosis method is proposed in this paper. Firstly, kernel entropy component analysis (KECA) method is used to achieve multi-phase partition adaptively, which makes the process data mapped into the high-dimensional feature space and then constructs the core entropy and the angular structure similarity. Then a multi-phase KECA failure monitoring model is developed by using the angular structure similarity as the statistic, which is based on the partitioned phases and the effective failure features by the KECA feature extraction method. A multi-phase batch process fault diagnosis method, which applies the multi-class support vector machines (MSVM) and fireworks algorithm (FWA), is proposed to recognize each sub-phase fault diagnosis automatically. The effectiveness and advantages of the proposed multi-phase fault diagnosis method are illustrated with a case study on a fed-batch penicillin fermentation process.