基于MCKD算法的轴承振动信号处理及故障诊断研究
Vibration Signal Processing and Fault Diagnosis of Bearing Based on MCKD Algorithm
周淑娟1
作者信息
- 1. 河南工业贸易职业学院汽车工程学院,河南 郑州 450000
- 折叠
摘要
为了提高轴承振动信号抗干扰能力,设计了一种基于最大相关峭度解卷积(MCKD)算法的轴承振动信号处理及故障诊断方法.利用相关系数-峭度方法确定IMF1与IMF2并完成重构降噪过程,利用MCKD算法实现重构信号的降噪,再以包络谱完成解调分析.研究结果表明:在105.5 Hz位置存在较明显的波峰,形成了与滚动体故障频率理论值相同结果,依然形成了明显的2倍频波峰,证明MCKD算法对滚动轴承进行初期故障诊断时具备更明显的优势.本研究有助于提高轴承的故障识别能力,也可拓展到其他的机械传动领域.
Abstract
In order to improve the anti-interference ability of bearing vibration signal,a bearing vibration signal processing and fault diagnosis method based on maximum correlation kurtosis deconvolution(MCKD)algorithm is designed.The correlation-Kurtosis method is used to determine IMF1 and IMF2 and complete the reconstruction noise reduction process.The MCKD algorithm is used to realize the noise reduction of the reconstructed signal,and the demodulation analysis is completed by the envelope spectrum.The research results show that there is a more obvious wave crest at the position of 105.5Hz,which forms the same result as the theoretical value of the fault frequency of the rolling element,and still forms an obvious double frequency wave crest,which proves that the MCKD algorithm has a more obvious advantage in the initial fault diagnosis of rolling bearings.This research is helpful to improve the fault identification ability of bearings,and can also be extended to other mechanical transmission fields.
关键词
轴承/振动信号/特征提取/最大相关峭度解卷积Key words
bearings/vibration signal/feature extraction/maximum correlation kurtosis deconvolutionn引用本文复制引用
基金项目
河南省高等学校重点科研项目(22A470005)
出版年
2024
NETL
NSTL
万方数据