一种井下瓦斯传感器故障辨识方法  

Fault Identification Method for Underground Gas Sensor

作　　者：乔维德 周晓谋[2] QIAO Wei-de;ZHOU Xiao-mou(Dept. of Scientific Research & Quality Control, Wuxi Open University, Wuxi, Jiangsu 214011, China;School of Mechanical & Electrical Engineering, China University of Mining & Technology, Xuzhou Jiangsu 221116, China)

机构地区：[1]无锡开放大学科研与质量控制处,江苏无锡214011 [2]中国矿业大学机电工程学院,江苏徐州221116

出　　处：《石家庄学院学报》2017年第3期46-52,共7页Journal of Shijiazhuang University

基　　金：无锡市"530"社会事业领军人才资助项目(2017/530/009)

摘　　要：鉴于目前煤矿井下瓦斯传感器故障辩识速度慢、辩识准确度不高等缺陷,提出基于小波包分解与砸BF神经网络的瓦斯传感器故障辨识方法.采用小波包分解提取瓦斯传感器故障特征向量并输入至RBF神经网络,应用粒子群-人工蜂群(PSO-ABC)算法优化砸BF神经网络结构参数,并通过大量的瓦斯传感器样本对砸BF神经网络模型进行训练和检测.实验分析表明:本方法的辨识速度快、诊断正确率高,为精准辩识瓦斯传感器故障提供一种更加科学高效的新途径.In view of the current coal mine gas sensor fault identification speed and identification accuracy ofdefects,this paper proposes a wavelet packet decomposition and gas sensor fault identification method based on RBFneural network.The wavelet packet decomposition is used to extract the fault feature vector of the gas sensor and input to the RBF neural network.The particle swarm artificial bee colony(PSO-ABC)algorithm is used to optimize thestructural parameters of RBF neural network,and the RBF neural network model is trained and detected by a largenumber of gas sensor samples.The experimental analysis shows that the identification speed of this method is fast,high rate of correct diagnosis,providing a new,more scientific and efficientway for accurate identification of gas sen原sor fault.

关 键 词：瓦斯传感器 小波包分解 RBF神经网络 粒子群-人工蜂群算法 故障辨识 

分 类 号：TP212[自动化与计算机技术—检测技术与自动化装置]