摘要
基于高密度小波变换对原始信号尺度划分更加精细的优势,将高密度小波变换、软阈值降噪和频谱分析相结合,提出了基于高密度小波变换的航空发动机滚动轴承故障诊断方法。该方法通过设定分解层数对信号进行高密度小波变换,得到每一尺度上的低频、中频、高频分量;对各分量软阈值降噪处理后进行频谱分析,进而实现故障特征频率的识别。利用仿真信号验证了高密度小波变换的有效性,通过航空发动机滚动轴承内圈故障和滚子故障工况下的试验信号进一步验证了该方法提取故障特征的能力,与传统小波变换方法的对比证明了该方法在抑制噪声干扰和故障特征频率识别方面的优势。
Based on advantage of high-density wavelet transform to divide the original signal scale more finely, a fault diagnosis method for aero-engine rolling bearings based on high-density wavelet transform is proposed by combining high-density wavelet transform, soft threshold denoising and spectrum analysis. In this method, the high-density wavelet transform of signal is carried out by setting the number of decomposition layers, and the low frequency, medium frequency and high frequency components on each scale are obtained. The spectrum analysis is carried out after soft threshold denoising of each component, and then the fault feature frequency is identified. Firstly, the validity of high-density wavelet transform is verified by simulation signals. Secondly, the test signals under the conditions of inner ring fault and roller fault of aero-engine rolling bearings further verify the ability of this method to extract the fault features. Finally, compared with traditional wavelet transform method, the advantages of this method in noise interference suppression and fault feature frequency identification are proved.
作者
黄姗姗
李志农
HUANG Shanshan;LI Zhinong(Nanchang Hangkong University,Nanchang 330063,China;Key Laboratory of Nondestructive Testing,Ministry of Education,Nanchang 330063,China)
出处
《轴承》
北大核心
2023年第2期19-25,共7页
Bearing
基金
国家自然科学基金资助项目(52075236)
江西省自然科学基金重点项目(20212ACB202005)
南昌航空大学研究生专项基金资助项目(YC2022-091)。
关键词
滚动轴承
故障诊断
小波变换
特征提取
频谱分析
rolling bearing
fault diagnosis
wavelet transform
feature extraction
spectrum analysis
