In　the　fault　diagnosis　of　bearings,　the　high　flexibility　of　the　asymmetric　Gaussian　chirplet　model　enables　the　adapted　dictionary-free　orthogonal　matching　pursuit　to　manifest　good　performance.　Since　this　method　does　not　rely　on　predetermined　dictionaries,　it　has　potential　advantages　as　well,　especially　for　the　impulses　caused　by　compound　faults　that　are　multiple　type＇s　combination.　Benefiting　from　the　sparse　representation　architecture,　the　fault　isolation　can　be　skillfully　converted　into　a　0-1　programming　problem　for　elements　selection　in　sparse　vector,　which　may　become　one　of　the　breakthroughs　in　solving　compound　faults’　isolation.　Consequently,　this　paper　attempts　to　give　a　solution　using　signal　processing.　Specifically,　the　maximum　entropy　deconvolution　adjusted　technique　is　used　for　preprocessing,　which　includes　noise　reduction　and　impulsiveness　enhancement.　Notch　filter　and　spectral　subtraction　is　utilized　for　impulses　screening　and　extraction,　and　spectrum　is　employed　for　fault　diagnosis.　Simulation　analysis　and　experimental　tests　verify　the　proposed　method,　whose　results　illustrated　the　potentiality　to　response　the　compound　fault　isolation　and　diagnosis.　©　2021