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 cracks 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 shafts 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 cracks 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 cracks degree on the supporting shaft.
Time series-Neural network Supporting shaft Fatigue crack Larger-scale overloaded
Li Xuejun Bin Guangfu Chu Fulei Xiao Dongming
Key Lab of Health Maintenance for Mechanical Equipment of Hunan Province,Hunan Science and Technolog Key Lab of Health Maintenance for Mechanical Equipment of Hunan Province,Hunan Science and Technolog Department of Precision Instruments and Mechanology,Tsinghua University,Beijing 100084,China
国际会议
西安
英文
2007-08-16（万方平台首次上网日期，不代表论文的发表时间）