滚动轴承工作环境比较复杂，现场测得的振动信号往往含有大量噪声且滚动轴承早期故障特征比较微弱容易被噪声所淹没，如何有效降低滚动轴承故障信号中的噪声准确提取故障特征是一个难题。将流形理论与对偶树复小波(Dual-tree complex wavelet transform, DTCWT)方法结合，提出一种对偶树复小波流形域降噪方法。将轴承振动信号进行对偶树复小波分解构造高维信号空间，然后利用最大方差展开流形算法(Maximum variance unfolding, MVU)提取高维信号空间中的真实信号子空间，去除噪声子空间，充分利用了MVU的非线性特征提取能力以及DTCWT的完全重构特征和平移不变性。运用仿真数据和滚动轴承工程信号对降噪方法进行检验，结果表明DTCWT_MVU可以有效消除轴承信号中的噪声成分，保持信号特征波形，提高信噪比，具有较强的工程使用价值和通用性。

The rolling element bearing works in a very complex environment. Early bearing fault features are relatively weak and easily to be overwhelmed by the large amount of noise. How to reduce the noise and extract the fault features accurately is a difficult problem. Novel noise reduction method is put forward based on dual tree complex wavelet transform (DTCWT) and maximum variance unfolding (MVU). The DTCWT is applied to rolling-bearing vibration signals to structure the high dimensional signal space. The MVU is used to extract the real signal subspace from high dimensional signal space and eliminate the noise interference of the noise subspace. The proposed method makes full advantage of the nonlinear feature extraction ability of the MVU algorithms and the perfect reconstruction and near shift invariance of the DTCWT. The simulation and the bearing engineering signal is carried out to inspect the noise reduction method, and the results demonstrate that DTCWT_MVU can effectively eliminate the noise component of the bearing signal and keep its characteristic waveform and improve signal to noise ratio (SNR) with the practical and generality.