Time Series-Neural Networks Diagnostics for the Fatigue Crack of the Large-scale Overloaded Supporting shaft

The time series-neural network is attempted to be applied in research on diagnosing the fatigue crack´s degree based on the analysis of characteristics on the supporting shaft. By analyzing the characteristic parameter which is easy to be detected from the supporting shaft´s exterior, the time series model parameter which is hypersensitive to the situation of fatigue crack is the target input of neural network, and the fatigue crack´s degree value of supporting shaft is the output. The BP network model can be built and trained after the structural parameters of network are selected. Furthermore, choosing the other two different group data can test the network. The test result will verify the validity of the BP network model. The result of experiment shows that the method of time series-neural network is effective to diagnose the occurrence and the development of the fatigue crack´s degree on the supporting shaft.