In order to extract fault features of a weak signal from the strong noise and maintain signal smoothness,a new method of denoising based on the algorithm of balanced orthogonal multiwavelets is proposed.Multiwavelets have several scaling functions and wavelet functions,and possess excellent properties that a scalar wavelet cannot satisfy simultaneously,and match the different characteristics of signals.Moreover,the balanced orthogonal multiwavelets can avoid the Gibbs phenomena and their processes have the advantages in denoising.Therefore,the denoising based on the algorithm of balanced orthogonal multiwavelets is introduced into the signal process.The algorithm of balanced orthogonal multiwavelet and the implementation steps of this denoising are described.The experimental comparison of the denoising effect between this algorithm and the traditional multiwavelet algorithm was done.The experiments indicate that this method is effective and feasible to extract the fault feature submerged in heavy noise. In order to extract fault features of a weak signal from the strong noise and maintain signal smoothness, a new method of denoising based on the algorithm of balanced orthogonal multiwavelets is proposed. Multiwavelets have several scaling functions and wavelet functions, and possess excellent properties that a scalar wavelet can not satisfy simultaneously, and match the different characteristics of signals. More than, the balanced orthogonal multiwavelets can avoid the Gibbs phenomena and their processes have the advantages in denoising. Herefore, the denoising based on the algorithm of balanced orthogonal multiwavelets is introduced into the signal process. The algorithm of balanced orthogonal multiwavelet and the implementation steps of this denoising are described. the experimental comparison of the denoising effect between this algorithm and the traditional multiwavelet algorithm was done. experiments indicate that this method is effective and feasible to extract the fault feature submerg ed in heavy noise.