Multivariable statistical process control(MSPC)is developed in order to extract useful information from process data and utilize them for process monitoring.But when the change in process feed load or product composition happens

the conventional MSPC method does not function well for the process with multiple operation modes.In order to solve these problems

a novel process monitoring method is proposed based on multiple model external analysis and Greedy-KP1M.First

based on the traditional external analysis

multiple models modeling method is introduced to have a better performance. The multiple operation modes of process are eliminated by multiple models external analysis

and the residual error is got for monitoring.Then

to monitor the residual error

the method called Kernel Possibilistic one-Mean clustering(KP1M)is proposed.KP1M has a good ability to monitor nonlinear process.Its performance is similar with support vector data description(SVDD).But the computation complexity of KP1M is far less than SVDD’s.Moreover

to reduce the computation complexity furthermore

Greedy method is adopted to extract the feature samples for KP1M modeling.In the end

the proposed method is applied to monitor the TE(Tennessee Eastman)process and the ethylene cracking furnace to show its efficiency.