Abstract
On the basis of the characteristic parameters selected from the fault sonic signals of cracking hammer with artificial diamond by means of with time series analysis and time domain statistics, three-layer artificial neural network is trained by an improved BP algorithm. The results state that the fault sonic signals can be identified by trained network system precisely.
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LI Kai-yang, HU Yao-gai, ZHONG Yu-ning. The fault sonic diagnosis of the pressure hammer with artificial diamond[J].Wuhan University Journal of Nat Sci, 1999,4(1): 150–154.
HU Yao-gai, LI Kai-yang, ZHGON Yu-ning. An improved BP algorithm of neural networks neural networks[J].Journal of Wuhan University (Natural Science Edition), 1999,45(1): 25–29 (Ch).
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Foundation item: Supported by the National Natural Science Foundation of China
Biography: LI Kai-yang (1963-), male, Associate professor. Current research interest is in image processing.
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Kai-yang, L., Yao-gai, H. & Yu-ning, Z. Application of artificial neural networks in sonic diagnosis of cracking hammer with artificial diamond. Wuhan Univ. J. Nat. Sci. 4, 155–157 (1999). https://doi.org/10.1007/BF02841488
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DOI: https://doi.org/10.1007/BF02841488