Fault　diagnosis　of　rotating　machinery　is　of　great　importance　to　the　high　quality　products　and　long-term　safe　operation.　However,　the　useful　weak　features　are　usually　corrupted　by　strong　background　noise,　thus　increasing　the　difficulty　of　the　feature　extraction.　Thereby,　a　novel　denoising　method　based　on　the　tunable　Q-factor　wavelet　transform　(TQWT)　using　neighboring　coefficients　is　proposed　in　this　article.　The　emerging　TQWT　possesses　excellent　properties　compared　with　the　conventional　constant-Q　wavelet　transforms,　which　can　tune　Q-factor　according　to　the　oscillatory　behavior　of　the　signal.　Meanwhile,　neighboring　coefficients　denoising　is　adopted　to　avoid　the　overkill　of　conventional　term-by-term　thresholding　techniques.　Because　of　having　the　combined　advantages　of　the　two　methods,　the　presented　denoising　method　is　more　practical　and　effective　than　other　methods.　The　proposed　method　is　applied　to　a　simulated　signal,　a　rolling　element　bearing　with　an　outer　race　defect　from　antenna　transmission　chain　and　a　gearbox　fault　detection　case.　The　processing　results　demonstrate　that　the　proposed　method　can　successfully　identify　the　fault　features,　showing　that　this　method　is　more　effective　than　the　conventional　wavelet　thresholding　denoising　methods,　term-by-term　TQWT　denoising　schemes　and　spectral　kurtosis.