一种井下瓦斯传感器故障辨识方法

Fault Identification Method for Underground Gas Sensor

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.