Drilling state monitoring and fault diagnosis based on integrating neural network and evidence theory
Liao Ming-yan · Zhongguo Shiyou Daxue xuebao. Ziran kexue ban · 2007
State monitoring and fault diagnosis of drilling process is the significant support for safe working of drilling system.Based on information fusion theory,parameter subspace about drilling process parameters and neural subnet for primary fusion was firstly established.So the evidence supporting for different fault mode in drilling fault frames of discernment can be obtained.Then by using D-S evidence theory,the confidence interval of fault diagnosing results was improved by fusing the evidence body that was outputted by neural subnet,the status of drilling process was identified very well.The experimental results show that the complexity of neural network can be decreased and the efficiency of neural network can be improved by the fusion arithmetic of integrating neural network and evidence theory,the veracity about drilling parameters can be improved by fusing integrating fusion arithmetic.