Apply VGGNet-based deep learning model of vibration data for prediction model of gravity acceleration equipment
| Autor: | Lee, SeonWoo, Yu, HyeonTak, Yang, HoJun, Yang, JaeHeung, Lim, GangMin, Kim, KyuSung, Choi, ByeongKeun, Kwon, JangWoo |
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| Rok vydání: | 2020 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | Hypergravity accelerators are a type of large machinery used for gravity training or medical research. A failure of such large equipment can be a serious problem in terms of safety or costs. This paper proposes a prediction model that can proactively prevent failures that may occur in a hypergravity accelerator. The method proposed in this paper was to convert vibration signals to spectograms and perform classification training using a deep learning model. An experiment was conducted to evaluate the performance of the method proposed in this paper. A 4-channel accelerometer was attached to the bearing housing, which is a rotor, and time-amplitude data were obtained from the measured values by sampling. The data were converted to a two-dimensional spectrogram, and classification training was performed using a deep learning model for four conditions of the equipment: Unbalance, Misalignment, Shaft Rubbing, and Normal. The experimental results showed that the proposed method had a 99.5% F1-Score, which was up to 23% higher than the 76.25% for existing feature-based learning models. Comment: 15 pages, 10 figures "for associated publication of paper is as follow: Journal of Mechanics in Medicine and Biology, https://www.worldscientific.com/worldscinet/jmmb" |
| Databáze: | arXiv |
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