Aiming　at　the　non-linear　and　dynamic　characteristics　of　intermittent　process　data,　a　process　fault　monitoring　method　based　on　recurrent　autoencoder　(RAE)　is　proposed.　To　establish　monitoring　model,　an　autoencoder　is　constructed　by　using　long　short-term　memory　(LSTM)　recurrent　neural　network.　Compared　with　the　traditional　autoencoder,　the　proposed　method　can　effectively　extract　the　dynamic　correlation　information　between　time　series　samples.　Firstly,　a　three-step　expansion　method　combining　batch　expansion　and　variable　expansion　are　used　to　expand　the　batch　process　data　into　two　dimensions,　and　input　sequences　for　modeling　is　obtained　by　sliding　window　sampling.　Then,　LSTM　is　used　to　reconstruct　the　input　sequences　to　train　an　autoencoder　model.　Moreover,　the　Squared　prediction　error　(SPE)　statistics　are　constructed　based　on　reconstruction　error　to　achieve　on-line　monitoring.　Finally,　the　proposed　method　is　applied　to　penicillin　fermentation　process　for　simulation　experiment　and　recombinant　Escherichia　coli　fermentation　process　monitoring.　The　results　show　that　the　method　can　detect　faults　in　time　and　has　better　monitoring　performance.　©　All　Right　Reserved.