基于蚁群算法的矿井提升机减速器齿轮故障诊断

Mine hoist reducer gear failure diagnosis based on ant colony algorithm

在研究蚁群优化神经网络训练算法的基础上,建立了矿井提升机减速器齿轮故障诊断模型。根据实测数据,分析研究信号并提取信号特征值,并应用训练后的BP神经网络诊断齿轮故障,实验表明效果良好,该模型网络的收敛速度大大提高,避免陷入局部最优解,用于减速器齿轮故障诊断准确可靠。

On the basis of researching the optimization of neural network through ant colony algorithm,the mine hoist reducer gear failure diagnosis model is established. According to the measured data,the signal is analyzed and researched,and the characteristic value of signal is extracted to apply to the trained BP neural network diagnosis to gear failure. The experiment shows that the effect is good,the convergence speed of this model network is greatly increased to avoid local optimization solution,and it is precise and reliable in the reducer gear failure diagnosis.