Modern　industrial　processes　often　exhibit　large-scale　and　nonlinear　characteristics.　Incipient　fault　detection　for　industrial　processes　is　a　big　challenge　because　of　the　faint　fault　signature.　To　improve　the　performance　of　incipient　fault　detection　for　large-scale　nonlinear　industrial　processes,　a　decentralized　adaptively　weighted　stacked　autoencoder　(DAWSAE)　-based　fault　detection　method　is　proposed.　First,　the　industrial　process　is　divided　into　several　sub-blocks　and　local　adaptively　weighted　stacked　autoencoder　(AWSAE)　is　established　for　each　sub-block　to　mine　local　information　and　obtain　local　adaptively　weighted　feature　vectors　and　residual　vectors.　Second,　the　global　AWSAE　is　established　for　the　whole　process　to　mine　global　information　and　obtain　global　adaptively　weighted　feature　vectors　and　residual　vectors.　Finally,　local　statistics　and　global　statistics　are　constructed　based　on　local　and　global　adaptively　weighted　feature　vectors　and　residual　vectors　to　detect　the　sub-blocks　and　the　whole　process,　respectively.　The　advantages　of　proposed　method　are　verified　by　a　numerical　example　and　Tennessee　Eastman　process　(TEP).