基于精细复合多尺度模糊熵的往复压缩机轴承间隙故障特征分析方法

Feature Analysis Method for Oversized Bearing Clearance Faults of Reciprocating Compressor Based on Refined Composite Multiscale Fuzzy Entropy

针对往复压缩机故障信号呈现非线性、非平稳等特点,提出了基于精细复合多尺度模糊熵(RCMFE)的往复压缩机轴承间隙故障特征提取方法。在精细复合多尺度熵的基础上,结合模糊熵概念,提出了RCMFE方法,应用其量化信号非线性特性形成故障特征。白噪声和1/f噪声仿真信号分析结果表明:RCMFE熵值对数据长度不敏感,未定义熵出现概率小。以往复压缩机传动机构轴承间隙故障为研究对象,应用RCMFE实现其故障信号特征提取,并与多尺度模糊熵、复合多尺度模糊熵进行对比,该方法特征区分度显著,支持向量机故障识别准确率高于其他方法。

Aiming at the nonlinear and non-stationary characteristics of the reciprocating compressor fault signal,a method for reciprocating compressor bearing clearance fault feature extraction based on Refined Composite Multi-scale Fuzzy Entropy(RCMFE)was proposed.On the basis of refined composite multi-scale entropy,combined with the concept of fuzzy entropy,the RCMFE method was proposed,which was used to quantify signal nonlinear characteristics to form fault characteristics.The simulation analysis results of white noise and 1/f noise show that the entropy value of RCMFE is not sensitive to data length,and the probability of undefined entropy is small.The bearing clearance fault of a reciprocating compressor transmission mechanism was the research object.RCMFE was used to realize the feature extraction of fault signal,and compared with multi-scale fuzzy entropy and compound multi-scale fuzzy entropy.This method has significant feature discrimination and the support vector machine fault identification accuracy rate is higher than the other methods.