Gas sensor Fault Diagnosis Based on Wavelet Packet and RBF Neural Network Identification
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.
gas sensorwavelet packetSCM￣PSORBF neural networkfault diagnosis
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