基于小波包分解和MCKD的水泵轴承故障诊断方法
A Fault Diagnosis Method for Water Pump Bearings Based on Wavelet Packet Decomposition and MCKD Algorithm
蒋辉 1邱露鹏 2蒋强2
作者信息
- 1. 沈阳理工大学 自动化与电气工程学院,沈阳 110159;沈阳天眼智云信息科技有限公司,沈阳 110179
- 2. 沈阳理工大学 自动化与电气工程学院,沈阳 110159
- 折叠
摘要
针对水泵在实际应用中所处环境复杂、故障信号包含大量噪声难以提取的问题,提出了一种结合小波包分解和最大相关峭度解卷积(MCKD)的水泵轴承故障诊断方法.首先,应用小波包分解对原始信号进行分解,根据分解信号的信噪比和标准差选取合适的分量进行重构;然后,采用MCKD算法对重构信号降噪处理,突出信号中的有效周期冲击成分;最后,对处理好的信号进行包络谱分析,从包络谱中得到故障频率.实验结果表明,小波包分解和MCKD方法能够有效提取水泵轴承故障特征频率,可为工程实际应用提供参考.
Abstract
For the problem that the pump is in a complex environment in practical applications and the fault signal contains a lot of noise that is difficult to extract,a pump-bearing fault diagnosis method combining wavelet packet decomposition and maximum correlation kurtosis deconvolution(MCKD)is proposed.Firstly,wavelet packet decomposition is applied to decompose the original signal,and the appropriate components are selected for reconstruction according to the signal-to-noise ratio and standard deviation of the decomposed signal.Then the MCKD algorithm is used to reduce the noise of the reconstructed signal and highlight the effective periodic shock components in the signal.Finally,the processed signal is analysed by envelope spectrum,from which the fault fre-quency is obtained.The experimental results show that the wavelet packet decomposition and MCKD methods can effectively extract the characteristic frequency of water pump bearing faults,which can be used as a reference for engineering practical applications.
关键词
最大相关峭度解卷积/小波包分解/故障诊断/轴承Key words
maximum correlation kurtosis deconvolution/wavelet packet decomposition/fault diag-nosis/bearing引用本文复制引用
基金项目
辽宁省教育厅科学研究经费项目(LG202014)
出版年
2024
NETL
NSTL
万方数据