计算机工程与应用Issue(13)：10-14,65,6.DOI:10.3778/j.issn.1002-8331.1310-0378
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
摘要
Abstract
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Key words
ant colony algorithm/differential evolution algorithm/wind turbine/gearbox/fault diagnosis分类
信息技术与安全科学引用本文复制引用
尹玉萍,刘万军..基于AC-DE算法的风电机组齿轮箱故障诊断方法[J].计算机工程与应用,2014,(13):10-14,65,6.基金项目
国家自然科学基金（No.61172144）。 （）
