In　the　process　monitoring　and　fault　diagnosis　of　batch　processes,　traditional　principal　component　analysis　(PCA)　and　least-squares　(PLS),　are　assuming　that　the　process　variables　are　multivariate　Gaussian　distribution.　But　in　the　practical　industrial　process,　the　data　observed　of　process　variables　do　not　necessarily　be　the　multivariate　Gaussian　distribution.　Independent　component　analysis　(ICA),　as　a　higher-order　statistical　method,　is　more　suitable　for　dynamic　systems.　Observational　data　are　decomposed　into　a　linear　combination　of　the　independent　components　under　statistical　significance.　The　higher　order　statistics　will　be　extracted　and　the　mixed　signals　are　decomposed　into　independent　non-Gaussian　components.　Traditional　method　of　ICA　has　to　predefine　the　number,　of　independent　components.　This　paper　proposed　an　improved　MICA　method　of　realizing　the　automatically　choosing　the　independent　components　through　setting　the　threshold　value　of　the　negentropy.　The　method　can　solve　the　problem　of　predefining　the　number　of　independent　components　in　traditional　methods　and　meanwhile　it　reduces　the　complexity　of　the　monitoring　model.　The　proposed　method　is　used　to　do　the　process　monitoring　and　fault　diagnosis　of　penicillin　fermentation　and　the　results　verify　the　feasibility　and　effectiveness　of　the　method.