基于小波包与质心粒子群的齿轮箱故障诊断及应用

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振动与冲击 ›› 2016, Vol. 35 ›› Issue (11) : 191-195.

论文

钱林1，康敏1,2

作者信息 +

Gearbox fault diagnosis and application based on wavelet packet and centroid particle swarm optimization

QianLin1, KangMin1,2

Author information +

文章历史 +

摘要

针对齿轮箱振动信号中蕴含大量状态信息难以有效提取的问题，利用小波包分解对原始振动信号进行降噪及特征能量提取，通过BP神经网络实现故障的模式识别。针对神经网络收敛速度慢、易陷入局部最优值问题，提出利用简单、易行的质心粒子群算法对BP神经网络的权值和偏置进行优化。在粒子群算法中，通过设计种群质心和最优个体质心、根据粒子位置动态改变惯性权重，并将其引入粒子群算法的速度调整公式中，来构建质心粒子群算法。分别将该方法与基本粒子群算法、遗传算法应用在齿轮箱故障诊断中，通过比较表明该方法可以有效提高分类效率和准确率。

Abstract

For a large amount of state information in gearbox vibration signal is difficult to be effectively extracted, the wavelet packet decomposition was used to reduce noise and extract energy feature of the original vibration signal. Pattern recognition of fault was carried out with BP neural network. Considering the BP neural network of slow convergence speed and easily getting into local optimal value, improved centroid particle swarm algorithm (ICPSO) was proposed to optimize the weights and bias of BP neural network. In ICPSO, by introducing the design of center in population and the best individual mass, the dynamically change of inertia weight into speed adjustment formula to build ICPSO. ICPSO was compared with basic particle swarm algorithm and genetic algorithm in gearbox fault diagnosis test. The results show that ICPSO can effectively improve the classification efficiency and accuracy.

导出引用

钱林1，康敏1,2. 基于小波包与质心粒子群的齿轮箱故障诊断及应用[J]. 振动与冲击, 2016, 35(11): 191-195

QianLin1, KangMin1,2. Gearbox fault diagnosis and application based on wavelet packet and centroid particle swarm optimization[J]. Journal of Vibration and Shock, 2016, 35(11): 191-195

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