基于短时滑移模糊熵和LPP的轴承故障诊断

针对旋转机械设备的故障特征微弱和环境噪声强等问题,提出了一种基于短时滑移模糊熵和局部保留投影法(locality preserving projection,简称LPP)的故障特征提取方法。首先,通过对滑移截断短时序列的架构分析,引入多尺度复合模糊熵,获得信号在不同复合尺度下的特征信息和故障潜在特征,能准确反应信号复杂度和不确定性;其次,应用LPP流形降维并保留信号的局部数据特征,设计最优带通滤波器,对轴承振动信号进行故障冲击特征提取。仿真分析和实验数据结果验证了该方法在强背景噪声情况下降噪抑制方面的有效性,具有快速识别和提取滚动轴承的微弱冲击特征的能力。

Fault Diagnosis of Rolling Bearings Based on Short-Time Slipping Fuzzy Entropy and LPP

Aiming at extracting rotating machinery fault characteristics submerged in strong background noise, a new method based on refined composite multiscale fuzzy entropy and locality preserving projection (LPP) is proposed for fault diagnosis. Firstly, by introducing slip construction short-time series and refined composite multiscale fuzzy entropy, The feature information in different scale and the fault potential characteristics can be acquired, which accurately describe the complexity and uncertainty of the vibration signal. Secondly, LPP is applied to reduce dimension and retain local signal feature. Then the designed optimized bandpass filter successfully extract the fault feature of the rolling bearing, which is separately verified by the simulation signal and experimental data. It is verified by the simulation and experimental result that the proposed method shows better performance and advantage in restraining noise and recognizing weak shock features of rolling bearing.