基于MCKD和峭度的液压泵故障特征提取

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。

Fault Feature Extraction of Hydraulic Pump Based on Kurtosis and MCKD

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.