基于小波包和神经网络的瓦斯传感器故障诊断

Gas sensor fault diagnosis based on wavelet packet and neural network

针对瓦斯传感器常见的故障,提出了基于小波包和神经网络的故障诊断方法。通过对瓦斯传感器的输出信号进行三层小波包分解,得到8个不同频段的分解信号,再对其进行特征提取得到一个八维的特征向量,作为故障样本对三层神经网络进行训练,建立故障类型分类器,对瓦斯传感器故障进行诊断。仿真结果表明:该方法可以准确地诊断出故障类型。

Aimed at the common gas sensor fault, the approach of fault diagnosis based on wavelet packet and neural network is discussed. The output of gas sensor by three-layer wavelet packet is decomposed to achieve eight signals of different frequency bands. An 8-dimeusional eigenvector of vibrating signals was constructed for training 3-layer neural network. The fault type classifier is established for the gas sensor fault diagnosis. The simulation result shows that the proposed method can detect faults accurately.