The　development　of　low-speed　fault　diagnosis　methods　especially　in　offshore　wind　turbines　is　considered　of　utmost　importance　for　mainly　solving　two　challenges.　These　include　diagnosis　based　on　imbalanced　data　with　low　signal　to　noise　ratio　and　invariant　features　acquired　from　multi-sensors.　To　effectively　address　these　issues,　in　this　work,　an　improved　deep　belief　network,　termed　Scaled-minimum　Unscented　Kalman　Filter-aided　DBN,　was　proposed　for　processing　imbalanced　data　under　low-speed.　First,　a　Gramian　Angular　Summation　Field　was　designed　to　preserve　absolute　temporal　relation　in　time-series　for　2-D　feature　maps.　Second,　the　traditional　deep　belief　network　was　improved　by　using　a　Scaled-minimum　Unscented　Kalman　Filter　to　enhance　the　nonlinear　tracking　ability.　The　latter　can　make　the　feature　representation　of　2-D　feature　maps　dynamically　adapt　its　configuration　and　enhance　the　anti-noise　ability　of　the　diagnosis　model.　Wherein,　minimum　sigma　set　and　scaled　unscented　transform　were　introduced　to　improve　the　ability　of　discriminative　fault　features　in　imbalanced　data　with　low-speed,　making　the　diagnostic　model　more　efficient.　Two　different　low-speed　experimental　cases　were　conducted　to　analyse　the　performance　of　the　proposed　method.　From　the　extracted　results,　the　anti-noise　ability　to　diagnose　the　fault　in　imbalanced　data　was　demonstrated.　©　2024　Elsevier　Ltd