基于ASTFA和PMMFE的齿轮故障诊断方法

发布时间：2018-06-13 15:35

本文选题：故障诊断 + 自适应最稀疏时频分析　；参考：《振动工程学报》2016年05期

【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。
[Abstract]:A new method to reflect the complexity or nonlinearity of signals - multiscale fuzzy entropy partial mean (PMMFE) is proposed. PMMFE is proposed on the basis of multiscale fuzzy entropy. The multi-scale fuzzy entropy, although it contains the time pattern information on different scales, reflects the intrinsic characteristics of the signal, but it is extremely large for the signals with similar characteristics. The characterization of the partial scale is not ideal for.PMMFE to consider the fuzzy entropy of multiple scales, using the characteristics of the partial distribution of fuzzy entropy on different scales to quantify the complexity or nonlinearity of the signal, and more accurately reflect the characteristics of the signal. But the gear fault vibration signal in the gear box is a multi source vibration signal. An adaptive most sparse time-frequency analysis method (ASTFA) can effectively separate the source signal of the gear fault vibration according to the gear meshing frequency to determine the original signal of the gear fault vibration. The ASTFA and PMMFE are combined to the gear fault diagnosis. First, the gear fault in the gear box is separated by ASTFA. Secondly, the multi scale fuzzy entropy of the signal is calculated, and the result of PMMFE. experimental analysis based on the multiscale fuzzy entropy calculation shows that the method can effectively distinguish the gear fault and its type in the gear box.
【作者单位】：湖南大学汽车车身先进设计制造国家重点实验室;
【基金】：国家自然科学基金资助项目(51375152,51575168) 智能型新能源汽车国家2011协同创新中心、湖南省绿色汽车2011协同创新中心资助项目
【分类号】：TH132.41

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