When　we　use　the　traditional　multi-scale　independent　component　analysis　method　to　extract　independent　component　ICA　on　each　scale,　and　then,　using　the　ICA　decomposition　on　the　reconstructed　data　to　construct　monitoring　statistics,　however,　data　of　the　reconstruction　on　the　nature　was　already　independent　component,　it　is　meaningless　to　extract　them　by　ICA.　Focusing　on　the　shortcoming,　this　paper　proposes　a　MSICA-OCSVM　method　that　was　combined　with　Multi-scale　Independent　Component　Analysis　(MSICA)　and　One-class　Support　Vector　Machine　(OCSVM)　to　monitor　the　process.　First,　we　can　use　the　wavelet　transform　decomposition　to　monitor　data　at　different　scales.　And　then,　the　data　was　processing　by　threshold　denoising,　and　was　monitored　on　each　scale　extraction　by　using　ICA　independent　principal　component.　Subsequently,　we　can　use　the　wavelet　transform　coefficients　for　each　scale　would　scale　back　on　the　reconstruction　of　the　new　signal　matrix　(X)　over　cap.　Finally,　new　OCSVM　model　was　constructed　by　the　reconstructed　matrix　(X)　over　cap.　We　can　make　the　use　of　determined　hyper-plane　to　construct　a　nonlinear　statistic,　and　the　appropriate　control　limits　was　determined　by　using　kernel　density　estimation.　What　is　more,　this　method　is　applied　to　penicillin　fermentation　process　simulation　platform,　the　experimental　results　show　that　this　method　can　effectively　utilize　the　structure　information　data　compared　to　traditional　MSICA　fault　monitoring　method,　the　failure　rate　of　false　positives,　false　negative　rate　was　significantly　reduced.