摘要
针对共振解调方法需要事先获得带通滤波器参数的不足,提出了一种基于粒子群的自适应共振解调方法。该方法采用改进粒子群算法,以峭度和故障脉冲能量因子为优化指标,对带通滤波器的中心频率和带宽进行自适应寻优,并采用最优带通滤波器对信号进行滤波分析,提取出信号中的故障特征频率,完成故障诊断。数字信号仿真实验和故障轴承诊断试验结果表明,该方法能够在强背景噪声下有效提取出信号中故障冲击频率,完成故障诊断。
An adaptive resonant demodulation method based on particle swarm optimization is proposed to overcome the shortage of the band-pass filter parameters need to be obtained in advance.The method uses the improved particle swarm optimization algorithm to optimize the center frequency and bandwidth of the band-pass filter with the kurtosis and fault pulse energy factor as the optimization index,and uses the optimal band-pass filter to filter and analyze the signal.The frequency of the fault feature in the signal is extracted to complete the fault diagnosis.The digital signal simulation experiment and the fault bearing diagnosis test show that the method can effectively extract the fault impact frequency in the signal under strong background noise and complete the fault diagnosis.
作者
毛海波
周凤星
MAO Hai-bo;ZHOU Feng-xing(College of Information Science and Engineering,Wuhan University of Scienceand Technology,Wuhan 430081,China)
出处
《组合机床与自动化加工技术》
北大核心
2019年第9期46-49,共4页
Modular Machine Tool & Automatic Manufacturing Technique
基金
国家自然科学基金(61174106)
关键词
共振解调
滚动轴承
粒子群算法
resonance demodulation
rolling bearing
particle swarm optimization