The　nonlinear,　time　correlation,　and　non-Gaussian　features　in　data　present　significant　challenges　for　effective　fault　detection.　While　the　Gate　Recurrent　Unit　(GRU)　network　is　renowned　for　its　capacity　to　manage　time　correlation,　it　falls　short　in　capturing　non-Gaussian　features　in　process　data,　which　can　likely　lead　to　suboptimal　monitoring　results.　To　address　this　limitation,　the　Enhancement　Gate　Recurrent　Unit　(ENGRU)　is　developed　to　perfect　the　fault　detection　accuracy　of　the　network.　Specifically,　The　ENGRU　effectively　extracts　high　order　statistics　information　by　employing　the　overcompleted　independent　component　analysis　method,　thereby　augmenting　its　ability　to　capture　non-Gaussian　properties.　The　extracted　features　information　are　then　entered　into　the　ENGRU　model　further　to　uncover　additional　hidden　features　beyond　what　the　GRU　can　achieve.　The　ENGRU　network,　which　is　built　upon　the　extracted　characteristic　information,　even　farther　enhances　the　accuracy　of　the　fault　detection.　The　merits　of　the　proposed　model　are　demonstrated　by　comparing　it　with　excellent　fault　detection　algorithms　on　a　benchmark　platform.　©　2023　Elsevier　Ltd