| DIAGNOSTICS OF FATIGUE CRACK IN ULTERIOR PLACES OF LARGER-SCALE OVERLOADED SUPPORTING SHAFT BASED ON TIME SERIES AND NEURAL NETWORKS |
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| 作者姓名： | LI Xuejun Key Laboratory of Health Maintenance for Mechanical Equipment of Hunan Province Hunan Science and Technology University Xiangtan China |
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| 作者单位： | LI Xuejun Key Laboratory of Health Maintenance for Mechanical Equipment of Hunan Province，Hunan Science and Technology University，Xiangtan 411201，China Department of Precision Instruments and Mechanology，Tsinghua University，Beijing 100084，China BIN Guangfu Key Laboratory of Health Maintenance for Mechanical Equipment of Hunan Province，Hunan Science and Technology Univsity，Xiangtan 411201，China CHU Fulei Department of Precision Instruments and Mechanology，Tsinghua University，Beijing 100084，China |
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| 基金项目： | 国家自然科学基金;中国博士后科学基金 |
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| 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.
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| 关 键 词： | 神经网络 时间 支撑轴 疲劳裂痕 |
DIAGNOSTICS OF FATIGUE CRACK IN ULTERIOR PLACES OF LARGER-SCALE OVERLOADED SUPPORTING SHAFT BASED ON TIME SERIES AND NEURAL NETWORKS |
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| LI Xuejun Key Laboratory of Health Maintenance for Mechanical Equipment of Hunan Province,Hunan Science and Technology University,Xiangtan ,China.DIAGNOSTICS OF FATIGUE CRACK IN ULTERIOR PLACES OF LARGER-SCALE OVERLOADED SUPPORTING SHAFT BASED ON TIME SERIES AND NEURAL NETWORKS[J].Chinese Journal of Mechanical Engineering,2007,20(3):79-82. |
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| Authors: | LI Xueiun BIN Guangfu CHU Fulei |
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| Affiliation: | [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 |
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| Abstract: | 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 diagnosing 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 network 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. |
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| Keywords: | Neural network Time series Larger-scale overloaded Supporting shaft Ulterior place Fatigue crack NEURAL NETWORKS TIME SERIES BASED SHAFT SUPPORTING FATIGUE CRACK experiment method of time series model effective diagnose occurrence development result verify validity different group data test |
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