工矿自动化2017，Vol.43Issue(4)：37-41,5.DOI:10.13272/j.issn.1671-251x.2017.04.009
基于改进BP神经网络的矿用通风机故障诊断
Fault diagnosis of mine ventilator based on improved BP neural network
摘要
Abstract
In view of characteristics of complicated correlation of mine ventilator failure and symptom,a fault diagnosis method using BP neural network optimized by dynamic adaptation cuckoo search algorithm was proposed.The optimal initial parameters of neural network are solved by using global search ability of dynamic adaptation cuckoo search algorithm.Then,the BP neural network is trained to obtain the final fault diagnosis model.The example analysis results show that the method can effectively achieve fault diagnosis of mine ventilator and has the characteristics of fast convergence and high precision,and the diagnosis accuracy of the test sample is 92.5 ％.关键词
矿用通风机/故障诊断/动态适应布谷鸟搜索算法/BP神经网络Key words
mine ventilator/fault diagnosis/dynamic adaptation cuckoo search algorithm/BP neural network分类
矿业与冶金引用本文复制引用
孙慧影,林中鹏,黄灿,陈鹏..基于改进BP神经网络的矿用通风机故障诊断[J].工矿自动化,2017,43(4):37-41,5.基金项目
国家自然科学基金项目(61304080). （61304080）
