The　emission　signal　of　locomotive　bearings　is　non-stationary,　nonlinear　and　mixed　with　complex　noise　when　operating　online.　The　adaptive　decomposition　method　is　widely　employed　for　denoising　and　feature　extraction.　Variational　mode　decomposition　(VMD)　is　a　powerful　adaptive　decomposition　method,　but　the　number　of　modes　K　and　the　penalty　factor　α　are　difficult　to　set　in　advance　and　the　unreasonable　parameter　selection　may　probably　bring　faulty　feature　loss.　To　address　this　problem,　a　novel　hybrid　entropy,　which　includes　the　envelope　fuzzy　and　dispersion　characteristics　of　entropy,　is　proposed　to　describe　the　complexity　and　detect　dynamic　change　of　intrinsic　mode　functions　component　of　VMD.　Furthermore,　an　adaptive　parameter　selection　variational　mode　decomposition　(APSVMD)　based　on　the　novel　hybrid　entropy　is　presented　to　obtain　the　optimal　parameter　combination　(K　and　α).　Then　the　sensitive　intrinsic　mode　function　(IMF)　can　be　chosen　by　novel　hybrid　entropy　based　on　its　new　features.　In　order　to　verify　the　performance　of　the　proposed　diagnostic　approach,　the　locomotive　online　running　test　was　performed.　The　experimental　results　demonstrated　that　the　APSVMD　represents　the　signal　modes　explicitly　and　can　process　different　types　of　signals.　As　a　result,　different　kinds　of　bearings　faults　were　diagnosed　successfully.　©　2023　Elsevier　Ltd