传感技术学报2016，Vol.29Issue(9)：1400-1404,5.DOI:10.3969/j.issn.1004-1699.2016.09.018
基于邻域粗糙集与支持向量极端学习机的瓦斯传感器故障诊断
Gas Sensor Fault Diagnosis Based on Neighborhood Rough Set Combined with Support Vector Machine and Extreme Learning Machine
摘要
Abstract
In order to solve the problem that the gas sensor diagnosis speed is slow and the diagnosis accuracy is not high,this paper takes the common type gas sensor fault such as impact,drift,bias and periodic fault as research ob⁃ject and proposes a pattern classification and identification of the fault diagnosis of gas sensor method based on neighborhood rough set(NRS)combined with support vector machine and extreme learning machine(SVM-ELM). First of all,normalize the feature attribute of the gas sensor,the reduction set is formed via reducing the attribute di⁃mension with NRS information reduction theory,including key attributes of the gas sensor. Train SVM-ELM taking the reduction set for input data and recognize the fault patterns using test samples. Finally,through experiment con⁃trast analysis,this method has the features of fast training speed,high accuracy of classification,and the identifica⁃tion correct rate is more than 95%. It can significantly improve the effectiveness and accuracy of the fault diagnosis.关键词
瓦斯传感器/邻域粗糙集(NRS)/支持向量极端学习机(SVM-ELM)/故障诊断Key words
gas sensor/neighborhood rough set/Support Vector Machine and Extreme Learning Machine(SVM-ELM)/fault diagnosis分类
信息技术与安全科学引用本文复制引用
单亚峰,汤月,任仁,谢鸿..基于邻域粗糙集与支持向量极端学习机的瓦斯传感器故障诊断[J].传感技术学报,2016,29(9):1400-1404,5.基金项目
国家自然科学基金项目（51274118）；辽宁省科技攻关基金项目（2011229011）；辽宁省教育厅基金项目 （）
