Due　to　the　complex　dynamic　behavior　in　fermentation　process,　real-time　online　fault　monitoring　is　very　difficult.　In　this　paper,　an　ensemble　learning　method,　based　on　a　statistical　model　and　a　mechanism　model,　is　presented　to　monitor　the　fault.　First,　the　linear　and　nonlinear　information　which　have　great　effect　on　fault　monitoring　are　extract　by　principal　component　analysis　(PCA)　and　kernel　entropy　component　analysis　(KECA).　Second,　the　judgment　conditions　of　faulty　information　were　determined　by　the　dynamic　parameter　change　information　of　the　mechanism　model.　Third,　Bayesian　inference　is　used　to　transform　the　monitoring　statistics　into　fault　probabilities　to　integrate　the　monitoring　statistics.　Finally,　based　on　the　data　in　a　real　fermentation　process,　simulation　experiments　are　carried　out.　The　results　show　that　the　monitor　model　using　the　ensemble　learning　has　better　monitor　accuracy　than　some　other　methods.　©　2020　IEEE.