title: SOM neural network fault diagnosis method of polymerization kettle equipment optimized by improved PSO algorithm.
abstract: For meeting the real-time fault diagnosis and the optimization monitoring requirements of the polymerization kettle in the polyvinyl chloride resin (PVC) production process, a fault diagnosis strategy based on the self-organizing map (SOM) neural network is proposed. Firstly, a mapping between the polymerization process data and the fault pattern is established by analyzing the production technology of polymerization kettle equipment. The particle swarm optimization (PSO) algorithm with a new dynamical adjustment method of inertial weights is adopted to optimize the structural parameters of SOM neural network. The fault pattern classification of the polymerization kettle equipment is to realize the nonlinear mapping from symptom set to fault set according to the given symptom set. Finally, the simulation experiments of fault diagnosis are conducted by combining with the industrial on-site historical data of the polymerization kettle and the simulation results show that the proposed PSO-SOM fault diagnosis strategy is effective.
authors.affiliation: School of Electronic and Information Engineering, University of Science & Technology Liaoning, Anshan 114044, China ; National Financial Security and System Equipment Engineering Research Center, University of Science & Technology Liaoning, Anshan 114044, China.
authors.forename: Jie-sheng
authors.initials: JS
authors.lastname: Wang
authors.affiliation: School of Electronic and Information Engineering, University of Science & Technology Liaoning, Anshan 114044, China.
authors.forename: Shu-xia
authors.initials: SX
authors.lastname: Li
authors.affiliation: School of Electronic and Information Engineering, University of Science & Technology Liaoning, Anshan 114044, China.
authors.forename: Jie
authors.initials: J
authors.lastname: Gao
mesh_terms: D000465:Algorithms; D016571:Neural Networks, Computer; D058105:Polymerization