摘要
针对航空发动机主轴承发生故障时特征信息提取不充分的问题,提出一种基于循环提取有效信息的主轴承故障特征增强方法。该方法首先对原始振动信号进行小波包分解,计算得到各个节点分量的相关系数值和峭度值,将其进行归一化融合为一个综合参数P i;其次根据特征信息循环提取准则定义一个置信区间,该区间将所有节点分量划分为高信噪比信号、低信噪比信号和高噪信号3个部分;然后不断筛选出高信噪比信号直至达到终止条件;最后重构所有高信噪比信号,并进行包络解调提取出轴承微弱故障特征。经仿真信号验证,去噪信号的信噪比相对于去噪前提升了11.31 dB。基于航空发动机中介轴承模拟试验台所测数据开展了特征信息循环提取方法有效性的综合验证,并对某型航空发动机主轴承振动信号进行了分析。实践表明:该方法适用于强背景噪声干扰工况下滚动轴承的特征提取,能准确诊断航空发动机主轴承故障。
In response to the problem of insufficient feature information extraction when the main bearing of aircraft engine fails,a method for enhancing the fault characteristics of main bearings based on cyclic extraction of effective information is proposed.Firstly,the original vibration signals are decomposed using wavelet packet decomposition,and the correlation coefficient and kurtosis values of each node component are calculated and normalized,and then fused into a comprehensive parameter Pi.Secondly,a confidence interval is defined based on the feature information cyclic extraction criterion,which divides all node components into three parts:high signal-to-noise ratio signals,low signal-to-noise ratio signals,and high noise signals.Then,high signal-to-noise ratio signals are continuously selected until the termination condition is reached.Finally,all high signal-to-noise ratio signals are reconstructed,and envelope demodulation is performed to extract the weak fault characteristics of the bearings.Simulation signal verification shows that the signal-to-noise ratio of the denoised signal is improved by 11.31 dB compared to before denoising.The effectiveness of the feature information cyclic extraction method is comprehensively verified based on the data measured from a simulated test bench for intermediate shaft bearings in aircraft engines,and an analysis of the vibration signals of a certain type of aircraft engine main bearings is conducted.Practice shows that This method is suitable for feature extraction of rolling bearing under the condition of strong background noise interference,and can accurately diagnose the main bearing fault of aircraft engine.
作者
栾孝驰
赵俊豪
沙云东
佟鑫宇
张振鹏
Luan Xiaochi;Zhao Junhao;Sha Yundong;Tong Xinyu;Zhang Zhenpeng(Key Laboratory of Advanced Measurement and Test Technique for Aviation Propulsion System,Liaoning Province,School of Aero-Engine,Shenyang Aerospace University,Shenyang 110136,China)
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2024年第3期251-262,共12页
Chinese Journal of Scientific Instrument
基金
辽宁省教育厅系列项目(JYT2020010)
中国航发产学研合作项目(HFZL2018CXY017)资助。
关键词
滚动轴承
航空发动机
小波包分解
特征信息循环提取准则
故障特征增强
rolling bearings
aero engine
wavelet packet decomposition
feature information cycle extraction criteria
fault feature enhancement
