Under　complex　working　conditions　with　noise　interference,　the　fault　feature　of　planetary　gearbox　is　difficult　to　be　extracted　and　the　fault　mode　is　difficult　to　be　identified.　To　tackle　this　problem,　the　technologies　of　variable　multi-scale　morphological　filtering　(VMSMF)　and　average　multi-scale　double　symbolic　dynamic　entropy　(AMDSDE)　are　proposed　in　this　paper.　VMSMF　selects　Chebyshev　Window　as　the　structural　element　and　automatically　selects　the　optimal-scale　parameters　according　to　the　signal　characteristics　of　the　planetary　gearbox,　which　improves　the　filtering　accuracy　and　calculation　efficiency.　AMDSDE　fully　considers　the　correlation　between　various　state　modes.　Once　combined　with　relevant　knowledge　of　Mathematical　statistics,　the　algorithm　can　effectively　reduce　misjudgment.　Firstly,　the　turn　domain　resampling　(TDR)　is　used　to　transform　the　time　domain　signal　of　variable　speed　into　the　angle　domain　signal　that　is　not　affected　by　speed　change.　Secondly,　the　proposed　VMSMF　is　used　to　de-noise　the　vibration　signal,　and　the　fault　signal　with　a　high　signal-to-noise　ratio　is　obtained.　Finally,　AMDSDE　is　used　to　extract　the　entropy　value　of　the　fault　signal　and　judge　the　fault　type.　The　proposed　technology　is　verified　by　four　kinds　of　signals　collected　from　the　sun　gear　of　the　planetary　gearbox　under　non-stationary　working　conditions.