Multi-block Kernel Probabilistic Principal Component Analysis Approach and its Application for Fault Detection

In this paper, a decentralized fault diagnosis approach of complex processes is proposed based on multi-block kernel probabilistic principal component analysis :KPPCA). Under the probabilistic modeling framework, this paper introduced MBKPPCA into process monitoring and gave a qualitative analysis on the problems of determining the parameters in MBKPPCA. Efficient Expectation-Maximization algorithms were developed for parameter learning in models analysis and algorithm is proposed and applied to monitor largescale processes. By mapping nonlinear data into high dimensional space by kernel function, the method eliminated process nonlinear features. Electro-fused magnesia furnace study was provided to evaluate the modeling and performances of the new method.