Realizing　multisensor　signal　fusion　and　weak　feature　adaptive　extraction　is　a　challenging　task.　Therefore,　a　new　algorithm　called　tensor　singular　spectrum　decomposition　(SSD)　is　proposed　in　this　study　for　the　adaptive　decomposition　of　multisensor　time　series.　Traditional　tensor　decomposition　algorithms,　such　as　CANDECOMP/PARAFAC　(CP),　high　order　singular　value　decomposition　(HOSVD),　and　Tucker　decomposition,　are　derived　from　n-mode　product.　The　n-mode　product　essentially　uses　the　idea　of　matrices　to　deal　with　tensors,　given　that　it　defines　the　multiplication　between　matrix　and　higher　order　tensor,　thereby　creating　problems　of　nonpseudodiagonal　core　tensor　and　nonunique　decomposition　results　in　traditional　tensor　decomposition　algorithms.　To　this　end,　the　decomposition　of　the　original　tensor　signal　and　the　reconstruction　of　multisensor　component　signals　are　realized　in　this　study　by　combining　the　trajectory　tensor　construction,　superposition　of　the　Gaussian　function　spectral　model,　adaptive　iterative　optimization　of　embedding　dimension,　and　diagonal　average　method　on　the　basis　of　the　principle　of　tensor-tensor　order-preserving　multiplication.　The　proposed　algorithm　inherits　the　perfect　mathematical　theory　and　excellent　properties　of　matrix　SVD　in　processing　single　sensor　signals,　while　retaining　the　inherent　structure　and　coupling　relationship　between　multisensor　data　and　realizing　the　organic　fusion　and　adaptive　decomposition　of　multisensor　signals.　The　analysis　results　of　simulation,　experimental,　and　engineering　signals　showed　that　the　proposed　method　can　effectively　extract　weak　fault　quantification　features　hidden　in　original　multisensor　signals　compared　with　the　existing　methods.