To　achieve　reliable　and　automatic　anomaly　detection　(AD)　for　large　equipment　such　as　liquid　rocket　engine　(LRE),　multisource　data　are　commonly　manipulated　in　deep　learning　pipelines.　However,　current　AD　methods　mainly　aim　at　single　source　or　single　modality,　whereas　existing　multimodal　methods　cannot　effectively　cope　with　a　common　issue,　modality　incompleteness.　To　this　end,　we　propose　an　unsupervised　multimodal　method　for　AD　with　missing　sources　in　LRE　system.　The　proposed　method　handles　intramodality　fusion,　intermodality　fusion,　and　decision　fusion　in　a　unified　framework　composed　of　multiple　deep　autoencoders　(AEs)　and　a　skip-connected　AE.　Specifically,　the　first　module　restores　missing　sources　to　construct　a　complete　modality,　thus　advancing　the　secondary　reconstruction.　Different　from　vanilla　reconstruction-based　methods,　the　proposed　method　minimizes　reconstruction　loss　and　meanwhile　maximizes　the　dissimilarity　of　representations　in　two　latent　spaces.　Utilizing　reconstruction　errors　and　latent　representation　discrepancy,　the　anomaly　score　is　acquired.　At　decision　level,　the　model　performance　can　be　further　enhanced　via　anomaly　score　fusion.　To　demonstrate　the　effectiveness,　extensive　experiments　are　carried　out　on　multivariate　time-series　data　from　static　ignition　of　several　LREs.　The　results　indicate　the　superiority　and　potential　of　the　proposed　method　for　AD　with　missing　sources　for　LRE.