Application of deep convolution neural network in chaotic vibration identification
TANG Yusi1, WANG Weihao1, CUI Hanguo1, LIU Shuyong1, CHAI Kai2
1.College of Power Engineering, Naval University of Engineering, Wuhan 430033, China;
2.College of Naval Architecture and Ocean, Naval University of Engineering, Wuhan 430033, China
Abstract：In recognition of chaotic vibration signals, the calculation amount of chaotic characteristic index is large and time-consuming, so it is difficult to meet the real-time requirements. Here, an intelligent chaotic recognition method based on deep convolution neural network was proposed. Firstly, attractor graphs of different vibration signals were obtained using the phase space reconstruction technique. Then, structural parameters of the classic network model AlexNet were optimized and trained. Finally, the improved model was applied in intelligent recognition of chaotic signals. The results of simulated and actually measured signals showed that the proposed method is feasible, it can provide a useful reference for on-line chaos recognition.
唐宇思1，王伟豪1，崔汉国1，刘树勇1，柴凯2. 深度卷积神经网络在混沌振动识别中的应用研究[J]. 振动与冲击, 2021, 40(13): 9-15.
TANG Yusi1, WANG Weihao1, CUI Hanguo1, LIU Shuyong1, CHAI Kai2. Application of deep convolution neural network in chaotic vibration identification. JOURNAL OF VIBRATION AND SHOCK, 2021, 40(13): 9-15.
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