The　in-core　self-powered　neutron　detector　(SPND)　acts　as　a　key　measuring　device　for　the　monitoring　of　parameters　and　evaluation　of　the　operating　conditions　of　nuclear　reactors.　Prompt　detection　and　tolerance　of　faulty　SPNDs　are　indispensable　for　reliable　reactor　management.　To　completely　extract　the　correlated　state　information　of　SPNDs,　we　constructed　a　twin　model　based　on　a　generalized　regression　neural　network　(GRNN)　that　represents　the　common　relationships　among　overall　signals.　Faulty　SPNDs　were　determined　because　of　the　functional　concordance　of　the　twin　model　and　real　monitoring　systems,　which　calculated　the　error　probability　distribution　between　the　model　outputs　and　real　values.　Fault　detection　follows　a　tolerance　phase　to　reinforce　the　stability　of　the　twin　model　in　the　case　of　massive　failures.　A　weighted　K-nearest　neighbor　model　was　employed　to　reasonably　reconstruct　the　values　of　the　faulty　signals　and　guarantee　data　purity.　The　experimental　evaluation　of　the　proposed　method　showed　promising　results,　with　excellent　output　consistency　and　high　detection　accuracy　for　both　single-　and　multiple-point　faulty　SPNDs.　For　unexpected　excessive　failures,　the　proposed　tolerance　approach　can　efficiently　repair　fault　behaviors　and　enhance　the　prediction　performance　of　the　twin　model.