Statistical monitoring and fault diagnosing of multivariate processes based on nonlinear principal component analysis

Model-based fault detection and isolation (FDI) in plants of complex control systems has been a subject of tremendous research over the last three decades. Most common FDI approaches are based on analytical models of the systems which are often not available in practice for complex multivariate processes.; This thesis presents a multivariate statistical process monitoring (MSPM) and fault diagnosis approach based on nonlinear principal component analysis (NLPCA). A technique called NLPCA neural network is applied to model the process of interest. It addresses the linearity limitation of the PCA by assuming that the hidden principal components are nonlinear functions of the observed process variables; therefore, it is more effective in extracting the information from nonlinearly correlated variables than linear methods. A new statistic fault diagnosing scheme is also developed based on analyzing the distribution patterns of the process data in the nonlinear principal component feature space through the use of self-organizing feature mapping (SOFM) and vector quantization algorithms.; The proposed procedure is compared with observer-based methods and current statistical methods in performing process monitoring and fault diagnosis of linear and nonlinear processes. Its applications are illustrated on the diesel engine actuator benchmark system as well as the three-tank benchmark system. (Abstract shortened by UMI.)...