摘要
目的 解决航空发动机主轴承微弱故障特征在高背景噪声环境和变转速工况下难识别的问题,提出基于优化小波包分解的航空发动机主轴承故障特征增强方法。方法 首先通过计算阶次分析方法,将振动时域信号转化为振动角域信号;然后对振动角域信号进行小波包分解,并引入有效故障特征能量比和优化最大相关峭度解卷积方法对信号故障特征进行增强,通过循环迭代逐步提取故障特征;最后对信号进行包络分析,并与理论轴承故障阶次进行对比,实现轴承故障诊断。结果 通过对整机试车条件下航空发动机主轴承外圈压坑故障实验数据进行分析,验证了该方法能够有效增强振动信号中的故障特征信息。结论 与传统WPD方法相比,该方法可以有效增强主轴承故障特征阶次,实现高背景噪声环境和变转速工况下的故障诊断。
In order to solve the problem that weak fault features of main bearing of aero-engine are difficult to be identified in high background noise environment and variable speed condition,the work aims to propose an enhancement method of fault features of main bearing of aircraft engine based on optimized wavelet packet decomposition.Firstly,the vibration time domain signal was transformed into vibration angle domain signal by calculation order analysis method.Then,the vibration angle do-main signal was decomposed by wavelet packet,and the effective fault feature energy ratio and optimized maximum correlation kurtosis deconvolution method were introduced to enhance the signal fault features,and the fault features were gradually ex-tracted through cyclic iteration.Finally,the signal envelope analysis was carried out and the bearing fault diagnosis was realized by comparing with the theoretical bearing fault order.By analyzing the test data of the main bearing outer ring of the aircraft en-gine under test conditions,it was verified that the proposed method could effectively enhance the fault feature information in the vibration signal.The results show that compared with the traditional WPD method,the proposed method can effectively enhance the fault feature order of main bearing,and realize fault diagnosis under high background noise environment and variable speed.
作者
张振鹏
栾孝驰
沙云东
杨杰
赵奉同
ZHANG Zhenpeng;LUAN Xiaochi;SHA Yundong;YANG Jie;ZHAO Fengtong(Liaoning Key Laboratory of Advanced Test Technology for Aeronautical Propulsion System,Shenyang Aerospace University,Shenyang 110136,China;AECC Shenyang Engine Research Institute,Shenyang 110015,China)
出处
《装备环境工程》
CAS
2024年第9期42-49,共8页
Equipment Environmental Engineering
基金
省教育厅项目-面上项目(JYTMS20230249)
辽宁省属本科高校基本科研业务费专项。
关键词
主轴承
优化小波包分解
最大相关峭度解卷积
计算阶次分析
故障特征增强
故障分析
main bearing
optimized wavelet packet decomposition
maximum correlation kurtosis deconvolutio
calcula-tion order analysis
fault feature enhancement
fault analysis
