Neutron　detectors　in　nuclear　power　plants　(NPPs)　are　critical　for　system　stability,　yet　their　malfunctions　may　lead　to　false　alerts　and　misdiagnoses.　Multidetectors　deployed　in　diverse　positions　vary　with　the　nuclear　reactor　states　contained　spatial-temporal　variations　of　neutron　fluxes.　Existing　methods　seldom　concurrently　consider　intricate　spatial-temporal　correlations　and　gradual　state　variations　among　detectors.　This　study　proposes　a　detector-oriented　fault　detection　and　isolation　method　named　the　spatial-temporal　state　adaptation　model　(ST-SAM).　The　method　introduces　a　local-global　spatial-temporal　network　that　captures　the　potential　interdependencies　within　the　detector　topology.　To　minimize　cross-state　discrepancies　in　reactors,　ST-SAM　integrates　three　submodules:　a　signal　reconstructor　to　enhance　the　specific-state　variation　representation;　a　correlation　alignment　to　mitigate　interstate　feature　discrepancies;　and　an　adversarial　discriminator　to　extract　spatial-temporal　state-invariant　features.　Leveraging　the　parallel　detection　strategy,　ST-SAM　effectively　detects　and　isolates　faulty　detectors,　preventing　fault　propagation　on　subsequent　diagnosis.　Experiments　on　ex-core　and　in-core　neutron　detectors　in　real-world　NPPs　with　simulated　faults　verify　that　the　ST-SAM　outperforms　various　state-of-the-art　methods　in　terms　of　signal　reconstruction　and　fault　detection.