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Multi-load Fault Diagnosis of Vibration Signal Based on Improved Genetic Neural Network
WANG Xin;YU Hong-liang;ZHANG Lin;DUAN Shu-lin;HUANG Chao-ming
2011, 31 (4):
137-141.
DOI: 10.3969/j.issn.1006-1355-2011.04.032
According to the motion law of diesel engine valve, the characteristic vector of cylinder-cover’s vibration signal is extracted by wavelet packet decomposition. For multi-load fault diagnosis, the hidden layer node number, weights and threshold of the back propagation genetic algorithms are optimized by binary and real value hybrid coding. Experiment results show that the method has obvious advantages on multi-load vibration signal fault diagnosis. It is able to improve the network learning ability, convergence speed and accuracy of detection.
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