传感技术学报Issue(2)：278-283,6.DOI:10.3969/j.issn.1004-1699.2015.02.023
基于小波包和RBF神经网络的瓦斯传感器故障诊断∗
Gas sensor Fault Diagnosis Based on Wavelet Packet and RBF Neural Network Identification
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 RBF neural network that is optimized by subtractive clustering and particle swarm optimization algorithm. Use three layer wavelet packet decomposition technologies to get the coefficients of each node,and adopt some cutting algorithm to improve the transient signal features of the fault,and then obtain the optimal energy spectrum. Next,use SCM￣PSO algorithm to optimize RBF neural network and make the particles search faster and easier to find the global optimal solution. Finally,through experiment contrast analysis,this method has the features of fast training speed,high accu￣racy of classification,and the identification correct rate is more than 95%. It can significantly improve the effective￣ness and accuracy of the fault diagnosis.关键词
瓦斯传感器/小波包/SCM￣PSO/RBF神经网络/故障诊断Key words
gas sensor/wavelet packet/SCM￣PSO/RBF neural network/fault diagnosis分类
信息技术与安全科学引用本文复制引用
单亚峰,孙璐,付华,訾海..基于小波包和RBF神经网络的瓦斯传感器故障诊断∗[J].传感技术学报,2015,(2):278-283,6.基金项目
国家自然科学基金项目(51274118，70971059)；辽宁省科技攻关基金项目(2011229011)；辽宁省教育厅基金项目( L2012119) （51274118，70971059）
