【摘要】 针对目前齿轮箱系统在利用神经网络故障诊断时存在正确识别率低和依靠经验选择参数的问题,提出了基于粒子群优化BP网络的齿轮箱故障诊断方法。简要介绍利用齿轮振动原理提取特征参数建立故障模型,该模型以齿轮箱特征向量为输入、故障类型为输出,详细分析了通过BP神经网络、概率神经网络和粒子群优化BP神经网络实现齿轮箱故障诊断。仿真结果表明,BP神经网络对齿轮箱故障诊断收敛速度慢,故障识别率为82%;概率神经网络的模型故障诊断识别率依据经验选取spread值决定,故障识别率最大为98%;粒子群优化后的BP神经网络故障诊断分类识别率为100%且自适应能力强。更多还原

【Abstract】 Aiming at the problem of low correct recognition rate and relying on experience to select parameters in gear box fault diagnosis by using neural network, a fault diagnosis method of gear box based on particle swarm optimization BP network is pro-posed. In this paper, a fault model is established by extracting characteristic parameters from gear vibration principle. The model takes eigenvector of gear box as input and fault type as output. The fault diagnosis of gear box is realized by BP neural network,probabilistic neural network and particle swarm optimization BP neural network. The simulation results show that the convergence speed of BP neural network for gear box fault diagnosis is slow, and the recognition rate of fault diagnosis is 82 %. The recognition rate of probabilistic neural network model fault diagnosis is determined by selecting spreads based on experience, and the maximum recognition rate is 98 %. The recognition rate of BP neural network fault diagnosis based on particle swarm optimization is 100 %and adaptive ability is strong.更多还原