A translation-invariant based adaptive threshold denoising method for mechanical impact signal is proposed. Compared with traditional wavelet denoising methods, it suppresses pseudo-Gibbs phenomena in the neighborhood of signal discontinuities. To remedy the drawbacks of conventional threshold functions, a new improved threshold function is introduced. It possesses more advantages than others. Moreover, based on utilizing characteristics of signal, a adaptive threshold selection procedure for impact signal is proposed. It is data-driven and level-dependent, therefore, it is more rational than other threshold estimation methods. The proposed method is compared to alternative existing methods, and its superiority is revealed by simulation and real data examples. A translation-invariant based adaptive threshold denoising method for mechanical impact signal is proposed. Compared to traditional wavelet denoising methods, it suppresses pseudo-Gibbs phenomena in the neighborhood of signal discontinuities. To remedy the drawbacks of conventional threshold functions, a new improved threshold function It is based on the characteristics of signal, an adaptive threshold selection procedure for impact signal is proposed. It is data-driven and level-dependent, therefore, it is more rational than other threshold estimation The proposed method is compared to alternative existing methods, and its superiority is revealed by simulation and real data examples.