To　solve　the　problems　that　multi-way　independent　component　analysis　(MICA)　need　to　assume　process　variables　conforming　non-Gaussian　distribution　and　the　monitoring　statistics　based　on　the　Mahalanobis　distance　of　MICA　will　cause　reduction　in　fault　detection　rate,　a　new　monitoring　method　based　on　Multi-way　Independent　Component　Analysis　and　One-Class　Support　Vector　Machines　(MICA-OCSVM)　was　researched.　Firstly,　the　independent　components　(ICs)　from　all　batches　of　batch　process　were　extracted　by　MICA.　Secondly,　OCSVM　was　used　to　model　for　all　batches＇　ICs　at　each　time,　separately.　Meanwhile,　decision　hyper-plane　of　the　OCSVM　model　was　chosen　to　construct　monitoring　statistics.　Finally,　the　confidence　limits　were　determined　using　kernel　density　estimation　by　the　monitoring　statistics　calculated　from　all　modeling　data.　The　method　was　applied　to　fed-batch　penicillin　fermentation　process.　The　experiment　results　show　that　in　contrast　to　the　fault　detection　methods　based　on　traditional　MICA,　the　proposed　method　can　make　full　use　of　ICs＇　structure　information　regardless　of　the　distribution　of　process　variables　and　can　reduce　the　rate　of　misinformation　and　omission　effectively.