A hybrid fault diagnosis methodology with support vector machine and improved particle swarm optimization for nuclear power plants. (December 2019)

• Record Type: Journal Article
• Authors: Wang, Hang; Peng, Min-jun; Wesley Hines, J.; Zheng, Gang-yang; Liu, Yong-kuo; Upadhyaya, Belle R.
• Is Part Of: ISA transactions. Volume 95(2019)
• Page Start: 358
• Page End: 371
• Publication Date: 2019-12
• Subjects: Process fault diagnosis -- Hybrid strategy -- Support vector machine -- Particle swarm optimization -- On-line simulation model; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2019.05.016
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: The safety and public health during nuclear power plant operation can be enhanced by accurately recognizing and diagnosing potential problems when a malfunction occurs. However, there are still obvious technological gaps in fault diagnosis applications, mainly because adopting a single fault diagnosis method may reduce fault diagnosis accuracy. In addition, some of the proposed solutions rely heavily on fault examples, which cannot fully cover future possible fault modes in nuclear plant operation. This paper presents the results of a research in hybrid fault diagnosis techniques that utilizes support vector machine (SVM) and improved particle swarm optimization (PSO) to perform further diagnosis on the basis of qualitative reasoning by knowledge-based preliminary diagnosis and sample data provided by an on-line simulation model. Further, SVM has relatively good classification ability with small samples compared to other machine learning methodologies. However, there are some challenges in the selection of hyper-parameters in SVM that warrants the adoption of intelligent optimization algorithms. Hence, the major contribution of this paper is to propose a hybrid fault diagnosis method with a comprehensive and reasonable design. Also, improved PSO combined with a variety of search strategies are achieved and compared with other current optimization algorithms. Simulation tests are used to verify the accuracy and interpretability of research findings presented in this paper, which would be beneficial for intelligent execution of nuclear power plant operation. Highlights: The challenges of knowledge-based diagnosis are compensated by the proposed algorithm. Training data is simulated to resolve the issue of real data acquisition. Improved PSO with multiple search strategies is presented in this paper. The optimization of hyper-parameters of SVM by improved PSO is compared with others.