Drilling state monitoring and fault diagnosis based on multi-parameter fusion by neural network
Liao Ming-yan · Zhongguo Shiyou Daxue xuebao. Ziran kexue ban · 2007
State monitoring and fault diagnosis of complicated systems is the significant support for system safe working.The change of characteristic parameters in drilling accident was analyzed.A diagnosis flow chart of neural network was given.A steady diagnosis model of neural network was developed by training the neural network using sample data.The right recognition result of system's state can be gained by imputing new sample data of system's state.The network performance was improved by improving the network algorithm. The data processing results show that the multi-parameter fusion algorithm based on neural network can recognize the different drilling states very well and implement the state monitoring and fault diagnosis.