In　various　industrial　processes,　soft　sensors　have　become　important　tools　for　predicting　key　quality　variables.　However,　traditional　soft　sensor　models　only　use　data　from　the　same　sampling　moments　as　the　key　quality　variables,　thus　wasting　information　from　other　sampling　moments.　In　light　of　this,　a　novel　soft　sensor　model　named　triple-gated　bidirectional　variational　pyramid　network　(G3-BiVPN)　is　proposed.　Within　G3-BiVPN,　the　multirate　dataset　is　first　segmented　into　multiple　datasets　each　with　a　single　sampling　rate.　For　each　dataset,　a　corresponding　bidirectional　variational　autoencoder　(BiVAE)　is　utilized　for　feature　extraction.　BiVAEs　are　used　as　backbone　models　to　form　a　bidirectional　pyramid　structure.　A　triple　gating　mechanism　consisting　of　attention　gate　(AG),　temporal　gate　(TG),　and　spatial　gate　(SG)　is　integrated　into　BiVAEs　to　regulate　information　flow.　Information　can　be　flowed　bidirectionally　through　different　levels,　with　each　level　establishing　a　regression　relationship　with　the　key　quality　variables　and　selecting　the　optimal　level　as　the　final　output.　The　core　advantage　of　G3-BiVPN　lies　in　its　utilization　of　the　multirate　nature　of　the　data.　Finally,　the　efficiency　of　G3-BiVPN　has　been　validated　through　two　sets　of　real-world　industrial　process　data　with　multiple　sampling　rates.　©　2004-2012　IEEE.