Aiming　at　the　characteristics　of　dense　noise　sources,　cross　noise　frequency,　and　non-linearity　and　non-stationarity　of　the　acoustic　signal　inside　the　plunger　motor,　an　improved　wavelet　packet　threshold　denoising　combined　with　ensemble　empirical　modal　analysis　and　Hilbert　transform　is　proposed　as　the　feature　signal　extraction　method.　By　collecting　vibration　signals　of　both　fault　free　and　faulty　motors　under　normal　operation,　constructing　a　wavelet　packet　secondary　decomposition　structure　and　thresholding　to　analyze　and　denoise　the　collected　signals,　and　then　using　an　improved　signal　processing　method　to　obtain　the　true　characteristic　signal　frequency　of　the　plunger　motorThe　novelty　of　this　study　lies　in　the　integration　of　advanced　signal　processing　techniques　to　enhance　fault　detection　accuracy　by　effectively　handling　noise　and　extracting　characteristic　frequencies　in　complex　operational　environments.　Experimental　validation　shows　that　the　proposed　method　effectively　enhances　the　feature　extraction　of　plunger　motors,　providing　a　reliable　basis　for　fault　diagnosis.　©　The　Author(s)　2025.