The　burgeoning　development　of　next-generation　technologies,　especially　the　Industrial　Internet　of　Things　(IIoT),　has　heightened　interest　in　predictive　maintenance　(PdM).　Accurate　failure　forecasting　and　prompt　responses　to　downtime　are　essential　for　improving　the　industrial　efficiency.　Traditional　PdM　methods　often　suffer　from　high　false　alarm　rates　and　inefficiencies　in　complex　environments.　This　paper　introduces　a　predictive　maintenance　framework　using　identity　resolution　and　a　transformer　model.　Devices　receive　unique　IDs　via　distributed　identifiers　(DIDs),　followed　by　a　state　awareness　model　to　assess　device　health　from　sensor　signals.　A　sequence　prediction　model　forecasts　future　signal　sequences,　which　are　then　used　with　the　state　awareness　model　to　determine　future　health　statuses.　Combining　these　predictions　with　unique　IDs　allows　for　the　rapid　identification　of　facilities　needing　maintenance.　Experimental　results　show　superior　performance,　with　99%　accuracy　for　the　state　awareness　model　and　a　mean　absolute　error　(MAE)　of　0.062　for　the　sequence　prediction　model,　underscoring　the　effectiveness　of　the　framework.　©　2024　by　the　authors.