西安理工大学学报Issue(2)：138-143,6.
基于果蝇优化算法的多元质量控制故障模式诊断
A fault diagnosis for multivariate production process based on Fruit Fly Optimization Algorithm
摘要
Abstract
Neural network served as a representative of the mainstream intelligent fault mode diag-nosis method,has had such defects as long learning time,difficulty of convergence and easily plunging into a local optimal solution.Thus,a Fruit Fly Optimization Algorithm for multivari-able process fault diagnosis model is established in this paper,the principle and search advantage of Fruit Fly Optimization Algorithm (FOA)is emphatically analyzed and a multivariable process fault diagnosis model based on FOA algorithm is designed.The Fruit Fly Optimization Algorithm is used for analyzing control sample data in the automobile crankshaft production,and a contrast is made with the results obtained from the neural network model.And contrast results show that Fruit Fly Optimization Algorithm has a short training time,fast convergence rate and more accu-rate diagnosis result.关键词
多变量生产过程/果蝇优化算法/过程控制/故障诊断/BP 神经网络/质量控制Key words
multivariate production process/Fruit Fly Optimization Algorithm (FOA)/process control/fault diagnosis/BP artificial neural network/quality control分类
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杨明顺,梁艳杰,雷丰丹,刘永,杜少博..基于果蝇优化算法的多元质量控制故障模式诊断[J].西安理工大学学报,2015,(2):138-143,6.基金项目
国家自然科学基金资助项目（60903124）；陕西省教育厅科学研究计划资助项目（14JK1521）；西安理工大学青年科技创新团队建设计划资助项目（102-211408）。 （）
