Apply VGGNet-based deep learning model of vibration data for prediction model of gravity acceleration equipment

• URL: http://arxiv.org/abs/2005.10985v2
• Date: Wed, 19 Aug 2020 02:49:31 GMT
• Title: Apply VGGNet-based deep learning model of vibration data for prediction model of gravity acceleration equipment
• Authors: SeonWoo Lee, HyeonTak Yu, HoJun Yang, JaeHeung Yang, GangMin Lim, KyuSung Kim, ByeongKeun Choi, and JangWoo Kwon
• Abstract summary: This paper proposes a prediction model that can proactively prevent failures that may occur in a hypergravity accelerator. The method was to convert vibration signals to spectograms and perform classification training using a deep learning model. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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