Abstract:

According to the non-linear and non-stationary characteristic of reciprocating compressor vibration signal, a fault feature extraction method based on adaptive empirical wavelet transform and mathematical morphology filtering is proposed. The method begin with using empirical wavelet transform, adaptively segments the Fourier spectrum by extracting the scale-space curve 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 compact support in Fourier spectrum. After segmenting the Fourier spectrum, the method filters the AM-FM components based on mathematical morphological. Different from other method, the structure elements in this paper are constructed according to the impact characteristics of reciprocating compressor. At last the method quantitatively analyzes AM-FM components by using the multiscale fuzzy entropy in order to identify and classify the fault of reciprocating compressor. Experiments and calculation results prove that this method can effectively diagnose the fault of reciprocating compressor.

摘要：

针对往复机械振动信号具有复杂非线性、非平稳等特性，使用一种基于小波框架的自适应经验小波变换和以集合角度处理信号的形态学滤波来进行往复机械故障特征提取。首先使用自适应经验小波变换通过构造尺度空间曲线对傅里叶频谱进行划分，构造合适的正交小波滤波器组以提取具有紧支撑傅里叶频谱的AM-FM成分；然后根据往复机械振动信号冲击性的特点，基于信号本身特性构造形态学结构元素，对提取出的模态进行状态自适应形态学滤波；最后使用多尺度模糊熵对模态进行定量分析并对故障进行识别。将该方法应用到实测数据中，实验结果验证了该方法的有效性，该方法可以准确对往复压缩机气阀故障进行识别。

关键词: 振动与波, 经验小波变换, 自适应性, 形态学滤波, 故障诊断