基于GA的知识获取方法及在故障诊断中的应用

Knowledge Acquisition Based on Genetic Algorithms and Its Application in Fault Diagnosis

从优化角度出发,定义一个新的指标函数,并提出一种基于遗传算法的机器学习方法,该方法能够从学习实例中总结出有用的知识。针对该优化模型,用一种新型的遗传算法——带有染色体性别的遗传算法(GACD)对其进行优化,并将该方法应用于旋转机械故障诊断知识获取过程,仿真实验结果说明该方法是比较有效的。

A novel machine learning algorithm is proposed based on optimization model, which can obtain knowledge from learning instances. In order to avoid the local optimum, a novel genetic algorithm genetic algorithm with chromosome differentiation (GACD) is proposed. It is used to the knowledge acquisition for fault diagnosis. Simulation results show that the algorithm is suitable for that purpose.