Abstract
Regarding the diversity and complexity of the road conditions on the soft sediment seabed, in order to improve the driving control precision of the caterpillar nodule collector, this study, focusing on the noise disturbance of the nodule collector body, adopts the methods of wavelet packet decomposition algorithm and Hilbert-Huang transformation algorithm to reconstruct the nodule collector body vibration signals, which are targeted by Hilber- Huang transformation algorithm to reach IMF fraction in the process of Empirical Mode Decomposition. Therefore, through the Hilbert spectrum analysis of IMF component, IMF component power characteristics are achieved, the available IMF component is optioned to reconstruct signals and the disturbance of the noise is eliminated. Comparing to the approach of wavelet decomposition, Hilbert-Huang transformation's analysis and algorithm of the collector body's vibration signals' reconstruction are more accurate, providing valid control parameter to control the drive of caterpillar nodule collector on the soft quality bottom of the sea more precisely.
Highlights
In the deep-sea mining system, caterpillar nodule collector is applied to collect polymetallic nodule, which, during its operation, is affected by coincident and complicated environmental factors, including wind, wave, sea current and submarine high-voltage, etc
Its performance in the fields of kinematics and dynamics is complex enough to bring immense challenge to the control system of the caterpillar nodule collector that runs on the soft sediment (Dai and Shao-Jun, 2009; Wang and Liu, 2004; Liu et al, 2003)
This study mainly aims to apply the approaches of wavelet decomposition and Hilbert-Huang changes (Wei et al, 2010; Rai and Mohanty, 2007; Li et al, 2008; Dong et al, 2008a, b) to deal with crawler-style set tub's vibration signals, in the meantime, those respectively received reconstructed signals and the practical vibration signals are compared and contrasted
Summary
The basic idea of EMD decomposition (Loutridis, 2004; Chang and Lee, 2009; Jia-Qiang et al, 2008):. Hilbert spectrum analysis of IMF components: Hilbert spectrum illustrates total vibration width (or power) distributed on every frequency value It discovers the width (or power) accumulation on total data sequence so that power’s distribution regularities in the scales of space (or time) are deliberately reflected during the physical process. Comparison between the reconstructed signals and real data: Ten sets of samples are selected and analyzed in order to compare collector’s body vibration signals’ decomposed and reconstructed wavelet signals with the comparative errors between Hilbert-Huang transformation reconstruction signals and the real data. Those for wavelet packet decomposition are 22.3, 14.3 and 17.3%, respectively. It is evident that the relative error of Hilbert-Huang transformation’s reconstructed signals is minor to those of wavelet packet’s decomposed and reconstructed signals
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