DIAGNOSTICS OF FATIGUE CRACK IN ULTERIOR PLACES OF LARGER-SCALE OVERLOADED SUPPORTING SHAFT BASED ON TIME SERIES AND NEURAL NETWORKS  

作　　者：LI Xueiun BIN Guangfu CHU Fulei 

机构地区：[1]Key Laboratory of Health Maintenance for Mechanical Equipment of Hunan Province,Hunan Science and Technology University, Xiangtan 411201, China [2]Department of Precision Instruments and Mechanology, Tsinghua University, Beijing 100084, China [3]Key Laboratory of Health Maintenance for Mechanical Equipment of Hunan Province,Hunan Science and Technology Univsity, Xiangtan 411201, China

出　　处：《Chinese Journal of Mechanical Engineering》2007年第3期79-82,共4页中国机械工程学报（英文版）

基　　金：This project is supported by National Natural Science Fundation of China (No. 50675066);Provincial Key Technologies R&D of Hunan, China (No. 05FJ2001);China Postdoctoral Science Foundation (No. 2005038006).

摘　　要：To improve the diagnosis accuracy and self-adaptability of fatigue crack in ulterior place of the supporting shaft, time series and neural network are attempted to be applied in research on diag-nosing the fatigue crack’s degree based on analyzing the vibration characteristics of 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 in ulterior place of the supporting shaft 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 net-work can be 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 and neural network are effective to diagnose the occurrence and the development of the fatigue crack’s degree in ulterior place of the supporting shaft.To improve the diagnosis accuracy and self-adaptability of fatigue crack in ulterior place of the supporting shaft, time series and neural network are attempted to be applied in research on diag-nosing the fatigue crack’s degree based on analyzing the vibration characteristics of 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 in ulterior place of the supporting shaft 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 net-work can be 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 and neural network are effective to diagnose the occurrence and the development of the fatigue crack’s degree in ulterior place of the supporting shaft.

关 键 词：Neural network Time series Larger-scale overloaded Supporting shaft Ulterior place Fatigue crack 

分 类 号：TH11[机械工程—机械设计及理论]