基于MCKD和峭度的液压泵故障特征提取  

Fault Feature Extraction of Hydraulic Pump Based on Kurtosis and MCKD

作　　者：何庆飞 王旭平 李禹生 HE Qingfei;WANG Xuping;LI Yusheng(School of Mechanical Engineering,Xijing University,Xi’an Shaanxi 710123,China;School of Operational Support,Rocket Force Engineering University,Xi’an Shaanxi 710025,China;Pinggao Group Co.,Ltd.,Pingdingshan Henan 467000,China)

机构地区：[1]西京学院机械工程学院,陕西西安710123 [2]火箭军工程大学作战保障学院,陕西西安710025 [3]平高集团有限公司,河南平顶山467000

出　　处：《机床与液压》2023年第1期208-211,共4页Machine Tool & Hydraulics

基　　金：国家科技重大专项基金项目(2017ZX04011010);国防预研基金项目(9140A27020309JB4701)。

摘　　要：液压泵早期故障信号具有非平稳性、强背景噪声、弱故障特征特点,故障特征难以有效提取。为此,提出基于自相关分析与最大相关峭度解卷积算法的齿轮泵故障特征提取方法,利用MCKD算法对采集信号去噪处理,增强信号中的原始冲击成分,提高信号的信噪比;基于峭度(或峭度绝对值,或峭度平方值)的特征信息提取方法,来度量机械信号的非高斯性程度,以表征机械设备的运行状态信息。试验结果证明:所提方法能够有效提取液压泵故障信号中的特征信息。The early fault signal of hydraulic pump is characterized by non-stationary,strong background noise and weak fault characteristics,so it is difficult to extract fault characteristics effectively.Accordingly,a fault feature extraction method of gear pump was proposed based on autocorrelation analysis and maximum correlation kurtosis deconvolution algorithm.The collected signal was denoised by using the MCKD algorithm to enhance the original impact component of the signal and improve the signal to noise ratio.A feature information extraction method based on kurtosis(or absolute or square kurtosis value)was proposed to measure the non-Gaussian degree of mechanical signals and characterize the operating state information of mechanical equipment.The experimental results show that the proposed method is effective in extracting characteristic information of hydraulic pump fault signals.

关 键 词：特征提取 最大相关峭度解卷积 峭度 液压泵 

分 类 号：TH165.3[机械工程—机械制造及自动化]