基于AC-DE算法的风电机组齿轮箱故障诊断方法  

Fault diagnosis method of wind turbine gearbox based on Ant Colony and Differential Evolution algorithm

作　　者：尹玉萍[1] 刘万军[2] 

机构地区：[1]辽宁工程技术大学电气与控制工程学院,辽宁葫芦岛125105 [2]辽宁工程技术大学软件学院,辽宁葫芦岛12510505

出　　处：《计算机工程与应用》2014年第13期10-14,65,共6页Computer Engineering and Applications

基　　金：国家自然科学基金(No.61172144)

摘　　要：提出一种基于蚁群和微分进化优化BP神经网络的风电机组齿轮箱故障诊断方法。将蚁群算法的信息素更新机制用于微分进化算法当中,提高微分进化算法的收敛速度,并利用微分进化个体更新方式改善蚁群算法的早熟问题,利用AC-DE算法对BP神经网络的权值和阈值进行优化,改善了BP神经网络算法陷入局部最优解的缺点,提高了神经网络的训练效率和收敛速度。经测试该方法诊断结果正确且精度高,表明了AC-DE优化BP神经网络用于风电机组齿轮箱故障诊断的有效性。A method based on BP neural networks trained by Ant Colony and Differential Evolution（AC-DE）algorithm is presented for fault diagnosis of wind turbine gearbox. The ant colony algorithm pheromone update mechanism for differ-ential evolution algorithm which improves the convergence speed of differential evolution algorithm and using differential evolution individual ways to improve the ant colony algorithm update premature problem, it can reduce the risk of BP neural network algorithm falling into local minimum, improve the training efficiency, and speed up convergence by using AC-DE algorithm to optimize the weights and bias of BP neural network. The new algorithm is applied to wind turbine gearbox fault diagnosis forecast, the method is tested and results of fault diagnosis are right. The validity and practicability of BP neural network algorithm trained by AC-DE algorithm for the wind turbine gearbox fault diagnosis are proved.

关 键 词：蚁群算法 微分进化算法 风电机组 齿轮箱 故障诊断 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置]