基于WPT-CEEMDAN-SVD的齿轮箱故障诊断
Study on Gearbox Gear Fault Diagnosis Based on Wavelet Packet Transform and CEEMDAN and SVD
李建航 1卢永杰 1郭锦萍 1康志新1
作者信息
- 1. 兰州交通大学 机电工程学院,甘肃 兰州 730070
- 折叠
摘要
针对在含噪声情况下难以精确地进行齿轮箱故障诊断的问题,将采集到的原始信号进行小波包分解,根据故障齿轮的啮合频率选取合适的小波包对信号进行重构,得到初步降噪信号;利用CEEMDAN对初步降噪信号进行分解,绘制各IMF分量的相关系数与峰度变化曲线图并选择相关系数较大的分量进行重构;通过奇异值分解对信号进一步降噪,并对最终信号频谱图对比分析,判断故障部位及类型.结果表明:该方法能根据实际需求有效提取到特定频率段内的特征频率谱线,优于直接对信号使用时频分析进行处理的结果.
Abstract
Accurate fault diagnosis of gearboxes under noise conditions is a difficult problem for gearbox fault di-agnosis.To solve this problem,Firstly,the acquired raw signal is decomposed into wavelet packets,according to the meshing frequency of the faulty gear and select a suitable wavelet packet to reconstruct the signal to obtain a preliminary noise reduction signal.Secondly,CEEMDAN is used to decompose the preliminary noise reduction signal.The correlation coefficient and kurtosis change curves of each IMF component are plotted and the compo-nents with large correlation coefficients are selected for reconstruction.Finally,the signal is further denoised by singular value decomposition,and the final signal spectrogram is compared and analyzed to determine the fault lo-cation and type.The results show that this method can effectively extract the characteristic frequency lines in the specific frequency band according to actual needs,which is better than the results of direct FFT analysis of signals.
关键词
故障诊断/自适应噪声完备集合经验模态分解/奇异值分解/小波包分解Key words
fault diagnosis/adaptive noise complete set empirical mode decompositions/singular value decom-position/wavelet packet transform引用本文复制引用
出版年
2024
NETL
NSTL
万方数据