基于粒子群的共振解调滚动轴承故障诊断研究

Research on Fault Diagnosis of Rolling Bearings for the Resonant Demodulation Method Based on Particle Swarm Optimization

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.