中文摘要：

提出一种新的反映信号复杂度或非线性度的方法——多尺度模糊熵偏均值（PMMFE）,PMMFE是在多尺度模糊熵的基础上提出的。多尺度模糊熵虽然包含不同尺度上的时间模式信息,反映了信号的内在特征,但是对于特征相近的信号,其在绝大部分尺度上的表征并不理想。PMMFE综合考虑多个尺度的模糊熵值,利用不同尺度上模糊熵值的偏态分布特性来定量表征信号的复杂度或非线性度,更加准确地反映信号的特征。但是齿轮箱中的齿轮故障振动信号是多源振动信号,需将齿轮振动本源信号分离出来才能进行特征提取。自适应最稀疏时频分析方法（ASTFA）根据齿轮啮合频率确定初始相位函数就可以有效分离齿轮故障振动本源信号。将ASTFA和PMMFE相结合用于齿轮故障诊断,首先采用ASTFA分离齿轮箱中的齿轮故障振动信号,其次计算该信号的多尺度模糊熵,再根据多尺度模糊熵计算PMMFE。实验分析结果表明该方法能够有效判别齿轮箱中的齿轮故障及其类型。

英文摘要：

A new method which is referred as partial mean multi-scales fuzzy entropy （PMMFE） is introduced to measure the complexity or nonlinearity. The PMMFE is proposed based on the multi-scales fuzzy entropy （MFE）. Although the MFE con- tains the time model information on different scales, and reflects the intrinsic characteristics of a signal, but the MFE is not i- deal in the vast majority of the scale for the signal with the similar characteristics. The PMMFE comprehensively considers the multi scalesr fuzzy entropy, using the skewness distribution of the different scalesr fuzzy entropy to quantitatively characterize the complexity or nonlinearity of a signal. It can reflect the characteristics of a signal more accurately. But for the gear fault in the gearbox, the fault vibration signal is multi source. It is required to separate the gear vibration signal for feature extraction. The adaptive and sparsest time-frequency analysis （ASTFA） method can effectively extract the gear fault source vibration sig- nal according to the gear mesh frequency to determine the initial phase function. The paper will combine the ASTFA and PMMFE for gear fault diagnosis. Firstly, extract the gear fault vibration signal by ASTFA. Secondly, calculate the signalrs MFE. Thirdly, calculate the signal＇s PMMFE according to the MFE. The experimental results show that the method can effectively achieve the gear fault diagnosis and identification.