In　nuclear　power　plants　(NPPs),　ex-core　neutron　detectors　are　deployed　around　reactor　cores　and　are　essential　for　reactor　stability,　but　their　deterioration　and　malfunction　can　cause　misperceptions　and　misdiagnoses.　Existing　fault　detection　seldom　accounts　for　global　spatial-temporal　coupling　relationships　implied　among　overall　detectors　and　uncertainty　under　transient　operations.　Thus,　we　propose　a　novel　detector-oriented　fault　detection　scheme　called　the　global-fused　dynamic　detection　(GFDD)　model,　established　by　the　global　spatial-temporal　graph　(GSTG),　moving-global　graph　convolution　(MGGC),　and　uncertainty-quantified　dynamic　detection　(UQDD).　To　enrich　informational　sources　and　disperse　faulty　propagation,　we　specifically　design　the　GSTG　for　characterizing　the　spatial-temporal　relationships　among　overall　detectors　and　the　MGGC　for　efficiently　capturing　global　high-level　features,　further　generating　multidetector　reconstructed　signals　and　residuals.　Through　calculating　dynamic　statistics　and　quantifying　uncertainty　under　varying　operating　conditions,　the　UQDD　identifies　faulty　detectors　and　corrects　erroneous　signals.　Experiments　on　steady　and　transient　states　from　a　real-world　NPP　with　simulated　faults　validate　that　the　GFDD　model　outperforms　various　state-of-the-art　methods　with　regard　to　signal　reconstruction　and　fault　detection.