经验小波变换(EWT)是一种新的自适应信号分解方法, 该方法继承了EMD 和小波分析方法的各自优点, 通过提取频域极大值点自适应地分割傅里叶频谱以分离不同的模态, 然后在频域自适应地构造带通滤波器组从而构造正交小波函数, 以提取具有紧支撑傅立叶频谱的调幅-调频(AM-FM)成分。本文将该方法引用到机械故障诊断中, 提出了一种基于经验小波变换的机械故障诊断方法, 并与EMD方法进行了对比分析。仿真结果表明, 经验小波变换方法明显优于EMD方法, 能有效地分解出信号的固有模态。与EMD 相比较, 该方法具有分解的模态少, 不存在虚假的模态, 计算量小, 且在理论上具有易理解性等特点。最后将该方法应用到转子碰磨故障诊断中, 实验结果进一步验证了该方法的有效性, 能够有效地揭示出碰磨故障数据的频率结构, 区分碰磨故障的严重程度。-Empirical wavelet transform (EWT) is a new self adaptive signal decomposition method. This method inherits the advantages of EMD and wavelet transform, adaptively segments the Fourier spectrum by extracting the maxima point in the frequency domain to separate the different modes, and then constructs adaptive band-pass filters in the frequency domain so as to construct orthogonal wavelet functions and extract AM-FM components that have a compact support Fourier spectrum. Here, the EWT is introduced in the mechanical fault diagnosis, and a new mechanical fault diagnosis method based on EWT is proposed. The EWT method is compared with the traditional EMD method. The simulation results show that the EWT method is obviously superior to the EMD method. The proposed method can effectively decompose the intrinsic modes of the signal. Compared with the EMD method, this method has some distinct advantages,such as less decomposed modes, no virtual modes, less calculation, easy to beunderstood in