The　most　common　and　effective　way　of　rotating　machinery　diagnostics　is　to　extract　fault　impact　features　from　the　vibration　signals　and　to　carry　out　further　processing.　As　for　planetary　gear　sets,　because　of　the　simultaneous　mesh　of　multiple　gears　and　the　effect　of　the　carrier　rotation,　the　fault　features　are　submerged　in　the　strong　harmonic　signals　and　other　noise.　Due　to　the　lack　of　prior　information　on　the　faults,　the　conventional　fault　diagnostic　methods　often　fail　to　achieve　satisfactory　results.　To　address　this　problem,　we　give　the　prior　information　of　a　chipped　planetary　gear　set　by　dynamics　simulation,　and　utilize　the　information　in　the　time　domain　to　improve　the　diagnosis　performance.　Firstly,　a　pure　torsional　lumped　parameter　model　is　used　to　simulate　the　system　vibration　response　with　different　degrees　of　failure.　Through　statistical　analysis,　the　margin　factor,　the　most　sensitive　indicator,　is　selected　as　prior　information　to　reflect　the　local　gear　fault　among　several　indexes.　Finally,　a　weighted　sparse　representation　method　based　on　the　prior　information　provided　above　is　proposed　to　extract　the　impact　features.　Moreover,　it　is　found　that　the　extracted　components　have　a　strong-weak　cyclic　impact　feature　in　the　chipped　planetary　gear　sets.　The　features　and　effectiveness　of　the　method　are　verified　by　an　experiment　on　a　planetary　gearbox　test　rig.　To　validate　the　superiority　of　the　proposed　method,　comparisons　are　made　among　several　state-of-the-art　feature　extraction　methods.