Sensor　faults　in　nuclear　power　plants　(NPPs)　have　the　potential　to　propagate　negative　impacts　on　system　stability,　leading　to　false　alarms　and　accident　misdiagnosis.　Existing　methods　seldom　concurrently　consider　complex　spatial–temporal　correlations　among　multi-type　sensors　in　the　primary　circuit.　This　study　presents　a　novel　sensor　fault　detection　and　isolation　scheme　named　the　knowledge-guided　spatial–temporal　model　(KGSTM),　using　the　knowledge-guided　recurrent　unit　(KGRU)　and　the　concurrent　detection　strategy.　To　organically　express　part　and　whole　interdependencies　from　inherent　sensor　layout,　several　graphs　are　specifically　designed　with　pertinent　domain　knowledge.　KGRU　consists　of　the　multi-graph　convolutional　network　(MGCN)　for　fusing　various　spatial　information　and　the　gate　recurrent　unit　(GRU)　for　extracting　dynamic　temporal　features,　further　obtaining　precise　reconstructed　signals　and　residuals.　The　concurrent　detection　strategy　can　explicitly　quantify　abnormal　behaviors　to　detect　and　isolate　faulty　sensors　by　characterizing　spatial–temporal　signal　variation.　Numerical　results　on　two　real-world　datasets　from　a　pressurized　water　reactor　(PWR)　with　simulated　faults　illustrate　that　the　KGSTM　has　superior　performance　over　various　state-of-the-art　methods　in　terms　of　signal　reconstruction　and　fault　detection.　©　2024　Elsevier　B.V.