Abstract
Vibration isolation technology makes a significant effect in the high-precision instruments field, however, the anti-interference technology at low-frequency and ultra-low frequency becomes the bottleneck of high-precision instrument development obstructively. The regular vibratory used in oil and gas exploration has a good effect on controlling the interfering signals above 6 Hz, but it doesn’t work well under 6 Hz. However, the Low-frequency excitation for hydrocarbon detection become a hotspot. In this paper, a hybrid vibration isolation method is proposed to suppress the interfering signal 6 Hz below and to improve the accuracy of the controllable vibratory excitation signal. A neural network (NN) with unique non-linear approximation capability is adopted to identify the vibration system and a NN predictive controller takes active control for the vibration systems. A simulation model is established using MATLAB/SIMULUNK. The simulation results showed that the proposed NN-based hybird isolation method can suppress the interference signals magnitude down by more than 92 % for 3–6 Hz interference signals, which put forward a novel effective anti-interference method for low-frequency vibration applications.
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Acknowledgement
The research work is supported financially by National 863 Project (Grand Number: 2012AA061201).
I would like to express my gratitude to Dr. Xiaoyan Chen and Dr. Zhigang Qu who helped me during the writing of this thesis.
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Li, S., Zhang, N., Tao, Z., Li, J., Wang, L., Ma, L. (2016). A Kind of Vibratory Isolation Algorithms Based on Neural Network. In: Bian, F., Xie, Y. (eds) Geo-Informatics in Resource Management and Sustainable Ecosystem. GRMSE 2015 2015. Communications in Computer and Information Science, vol 569. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49155-3_12
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DOI: https://doi.org/10.1007/978-3-662-49155-3_12
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