Principal　component　analysis　(PCA)　and　partial　least　squares　(PLS)　have　been　frequently　used　for　process　industry　monitoring;　however,　their　application　on　industrial　sites　is　limited　because　they　cannot　be　used　to　process　data　with　non-Gaussian　distribution.　Independent　component　analysis　(ICA)　has　become　a　powerful　modelling　method　for　non-Gaussian　process　monitoring.　However,　the　ICA-based　modelling　method　has　been　found　to　contribute　to　double　the　amount　of　data　loss　in　feature　extraction.　There　are　two　reasons　for　this.　First,　when　the　PCA　algorithm　is　used　to　whiten　the　original　data,　the　smaller　principal　component　is　discarded.　Second,　when　selecting　independent　components,　some　smaller　independent　components　will　be　discarded　according　to　the　evaluation　index.　The　abovementioned　two　data　feature　extraction　methods　may　discard　useful　information　for　fault　monitoring,　which　will　inevitably　lead　to　inaccurate　fault　monitoring.　To　solve　this　problem,　a　fault　monitoring　and　diagnosis　method　based　on　fourth　order　moment　(FOM)　analysis　and　singular　value　decomposition　(SVD)　is　proposed.　First,　the　fourth　order　moments　of　each　process　variable　were　constructed　separately.　Then,　the　data　space　of　the　fourth　order　moments　was　decomposed　by　singular　value　decomposition　to　establish　the　global　monitoring　statistics.　Finally,　the　contribution　diagram　was　drawn　and　the　fault　diagnosis　was　performed　based　on　the　global　monitoring　results.　The　proposed　method　was　applied　to　the　Tennessee　Eastman　(TE)　simulation　platform,　and　its　effectiveness　and　feasibility　were　verified　by　a　comparison　with　PCA　and　ICA.