摘要
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.
出处
《传感器与微系统》
CSCD
北大核心
2010年第5期80-82,共3页
Transducer and Microsystem Technologies
关键词
小波包分解
神经网络
故障诊断
瓦斯传感器
wavelet package transform
neural network
fault diagnosis
gas sensor
