一种基于非线性独立元分析(NICA)的化工过程监控方法
Industrial Process Monitoring and Fault Diagnosis with Nonlinear Independent Component Analysis
-
摘要: 针对实际工业过程数据中的非线性问题,研究了一种基于非线性独立元分析的多变量过程监控方法。该方法根据贝叶斯原理,构造多层感知器网络恢复过程数据,并以此建立过程的数学统计模型,对其进行实时监控。在大型工业设备仿真器TE上的应用表明了该方法的有效性,同时,在故障诊断方面也体现出了一定的优越性。Abstract: Considering the nonlinear characteristic of date in real industry processes,a multivariable process monitoring method based on nonlinear independent component analysis(NICA) is presented.With the help of Bayesian theorem,process data can be reconstructed by establishing multi-layer perceptrons,and statistical model of process in mathematics can be given for monitoring in real time.The proposed method is applied to the Tennessee-Eastman(TE) process.Simulation results show its availability and advantage in the aspect of fault diagnosis.