基于多参数信息融合筛选的滚动轴承振动信号特征提取与表征方法

Feature Extraction and Characterization of Rolling Bearing Vibration Signal Based on Multi Parameter Information Fusion and Screening

关键词:  航空发动机  滚动轴承  小波包分析  参数融合  故障诊断

Key words:  Aeroengine  Rolling bearing  Wavelet packet analysis  Parameter fusion  Fault diagnosis

摘要:

针对航空发动机机械系统滚动轴承故障诊断难的问题，提出一种基于多参数信息融合筛选的滚动轴承故障诊断方法。该方法采用小波包分解（WPD）强干扰环境复杂传递路径下测得的滚动轴承振动信号，得到若干个节点分量，以偏度值（Skew）、峭度值（Kurt）和排列熵（PE）多参数融合作为筛选指数（λ）对各节点分量进行筛选及重构，并进行包络解调识别故障特征信息。基于滚动轴承实验台测试数据及航空发动机中介轴承模拟试验台所测数据开展了振动信号提取与表征方法有效性的综合验证，并对某型航空发动机进行故障识别。结果表明：该方法可有效识别简单及复杂传递路径下滚动轴承故障特征，可作为航空发动机主轴轴承特征信息提取及故障诊断的有效方法之一。

Abstract:

Aiming at the difficult problem of rolling bearing fault diagnosis in aero-engine mechanical system， a rolling bearing fault diagnosis method based on multi-parameter information fusion screening was proposed. The method used wavelet packet decomposition （WPD） of the rolling bearing vibration signal measured under the complex transmission path of strong interference environment to obtain several nodal components， and the multi-parameter fusion of skewness value （Skew）， kurtosis value （Kurt） and permutation entropy （PE） was used as the screening index （λ） to screen and reconstruct each nodal component and perform envelope demodulation to identify the fault characteristic information. A comprehensive verification of the effectiveness of the vibration signal extraction and characterization method was carried out based on the test data of rolling bearing test bench and the measured data of aero-engine intermediate bearing simulation test bench， and the fault information was identified for a certain type of aero-engine. The results show that the method can effectively identify the fault characteristics of rolling bearings under simple and complex transmission paths， and can be used as one of the effective methods for aero-engine main shaft bearing feature information extraction and fault diagnosis.