PSO优化的最大峭度熵反褶积齿轮箱故障诊断

Maximum kurtosis entropy deconvolution gearbox fault diagnosis based on PSO

考虑到最小熵反褶积只对单一的异常振动信号很敏感,而且,滤波器的长度需要人工调控,提出了一种最大峭度熵反褶积方法,并将其应用于轴承故障诊断。考虑峭度熵具有突出连续冲击振荡的优点,选择峭度熵作为反褶积的目标函数。同时,利用峭度熵作为改进的局部粒子群优化算法的适应度函数,利用局部粒子群优化滤波器长度,使最大峭度熵反褶积在解卷积时自适应地调整滤波器长度,从而能够准确地提取出连续的脉冲信号。实验分析结果验证了该方法能够更加有效的提取连续脉冲信号的能力,提升了故障诊断的精度。

Considering that the minimum entropy deconvolution(MED) was only sensitive to a single abnormal vibration signal, and the length of the filter needed to be adjusted manually, a maximum kurtosis entropy deconvolution(MKSED) method was proposed and applied to bearing fault diagnosis. Considering that kurtosis entropy has the advantage of continuous shock oscillation, kurtosis entropy was chosen as the objective function of deconvolution. At the same time, kurtosis entropy was used as the fitness function of the improved local particle swarm optimization algorithm(LPSO), and LPSO was used to optimize the filter length, so that MKSED can adaptively adjust the filter length when deconvolution, so as to accurately extract the continuous pulse signal. The experimental results show that the method can extract continuous pulse signal more effectively and improve the accuracy of fault diagnosis.