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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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DIAGNOSTICS OF FATIGUE CRACK IN
ULTERIOR PLACES OF LARGER- SCALE OVERLOADED SUPPORTING SHAFT
BASED ON TIME SERIES AND NEURAL NETWORKS*
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.
Key words:
Neural network Time series Larger-scale
overloaded Supporting shaft
Ulterior place Fatigue crack |