The　condition　monitoring　of　the　main　bearing　(MB)　plays　a　crucial　role　in　the　maintenance　of　wind　turbines　(WT),　especially　for　direct-drive　wind　turbines　(DDWT).　However,　due　to　the　harsh　operating　environment　and　ultra-low　rotating　speed,　the　condition　monitoring　of　the　MB　is　still　a　challenging　issue.　In　this　study,　an　integrated　monitoring　scheme　using　acoustic　emission　(AE)　is　proposed　for　incipient　fault　detection　and　localization　of　MB.　First,　an　rotating　speed　estimation　approach　using　high-frequency　envelope　autocorrelation　(HFEA)　is　developed　to　recover　the　accurate　operating　speed　of　MB.　On　this　basis,　the　adapted　spectral　coherence　(ASC)　is　explored　to　identify　faulty　sources　buried　under　multiple　disturbances.　Finally,　an　effective　damage　localization　model　is　further　constructed　to　improve　maintenance　efficiency　in　practical　applications.　The　performance　of　the　proposed　methodology　is　evaluated　through　two　engineering　cases　with　natural　damages.　Compared　with　state-of-the-art　approaches,　the　proposed　method　can　not　only　effectively　detect　the　incipient　damage　of　the　MB,　but　also　accurately　determine　the　damage　location.　With　this　scheme,　the　inspection　efficiency　can　be　improved,　thus　it　may　provide　a　promising　tool　for　the　health　management　of　WT.　©　2022　Elsevier　B.V.