摘要
航空发动机主轴轴承故障诊断是发动机预测性维护与健康管理的重要组成部分。针对航发主轴轴承复合故障诊断难度大,易受其它振动干扰信号影响的问题,提出了一种基于规范相关分析盲信号提取和循环维纳滤波的航发主轴轴承复合故障诊断方法。通过优化规范相关分析准则,提出自适应共轭梯度算法实现盲信号提取,结合故障特征频率利用盲信号提取算法从观测信号中提取故障特征信号,将提取到的故障特征信号作为循环维纳滤波的期望信号,以此设计循环维纳滤器来恢复故障源信号。通过分析滤波后信号的包络谱,完成轴承复合故障的诊断。提出的方法克服了现有方法过分依赖轴承参数,且利用固定的数学模型获得的期望信号过于理想化而无法应用于实际工程的问题。分别利用仿真数据和试验机实测数据,验证了算法对主轴轴承复合故障诊断的有效性。
Fault diagnosis of an aero-engine spindle bearing is an important part of engine prognostics and health management.As is known, the diagnosis of the compound fault of an aero-engine spindle bearing is very difficult and easily affected by other vibration interference signals.We present a compound fault diagnosis method of an aero-engine spindle bearing based on blind signal extraction of canonical correlation analysis(CCA) and cyclic Wiener filtering.First, an adaptive conjugate gradient algorithm is proposed for extracting the blind signal by optimizing CCA criterion.Then, combined with the fault feature frequency, the blind signal extraction algorithm is used to extract the fault feature signal from the observed signal.The extracted fault fe12ature signal is regarded as the expected response of the cyclic Wiener filter.Finally, the cyclic Wiener filter is designed to recover the fault signal, and the envelope spectrum of the filtered signal is analyzed to complete the diagnosis of the bearing composite fault.The proposed algorithm overcomes the problem that the existing methods rely on the bearing parameters too much, and that the expected signal obtained from the fixed mathematical model is too ideal to be applied to practical engineering.Both simulated data and experimental data are used to verify the effectiveness of the algorithm in compound fault diagnosis.
作者
张伟涛
纪晓凡
黄菊
楼顺天
ZHANG Weitao;JI Xiaofan;HUANG Ju;LOU Shuntian(School of Electronic Engineering,Xidian University,Xi’an 710071,China;Research Institute of Guiyang Engine Design of Aero Engine Corporation of China,Guiyang 550081,China)
出处
《西安电子科技大学学报》
EI
CAS
CSCD
北大核心
2022年第6期139-151,共13页
Journal of Xidian University
基金
国家自然科学基金(62071350)。
关键词
循环维纳滤波器
滚动轴承
故障诊断
复合故障
包络谱
cyclic wiener filter
rolling bearing
fault diagnosis
compound faults
envelop spectrum
