基于离散粒子群优化算法的直升机减速器齿轮故障特征选择

粒子群优化算法自提出以来,由于其容易理解、易于实现,所以发展很快,在很多领域得到了应用。本文针对机械故障特征选择问题,提出基于离散粒子群优化(PSO)算法的特征选择方法,并在直升机减速器齿轮故障诊断中进行了应用。实验结果表明,离散PSO算法可以快速、有效的求得优化特征集,是求解故障特征选择问题的一个较好方法。

Discrete Particle Swarm Optimization Algorithm for Gearbox Fault Symptom Selection

Particle swarm optimization (PSO) has been developed rapidly and has been applied widely since it is easy to understand and realize.Aimed at mechanical fault symptom selection,the paper proposed a discrete Particle Swarm Optimization(PSO) algorithm.In order to improve the ability to escape from the local optimum,the mutation operator was added to the algorithm.The discrete PSO algorithm was applied to helicopter gearbox gear fault symptom selection.Symptom set extracted by wavelet packet transform method was presented by binary-coded particle.The accuracy evaluated by k-fold cross-validation of the SVM classifiers was used as the particle fitness.Experimental results indicate that the discrete PSO can get the same optimal symptom subset as that from genetic algorithm,and achieve the better diagnosis accuracy,but the compute time consumed is only 0.4 time that of genetic algorithm,so the discrete PSO is a more effective method for mechanical fault symptom selection problem.