Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Apply VGGNet-Based Deep Learning Model of Vibration Data for Prediction Model of Gravity Acceleration Equipment

Version 1 : Received: 24 December 2020 / Approved: 25 December 2020 / Online: 25 December 2020 (08:20:17 CET)

A peer-reviewed article of this Preprint also exists.

Lee, S.; Yu, H.; Yang, H.; Song, I.; Choi, J.; Yang, J.; Lim, G.; Kim, K.-S.; Choi, B.; Kwon, J. A Study on Deep Learning Application of Vibration Data and Visualization of Defects for Predictive Maintenance of Gravity Acceleration Equipment. Appl. Sci. 2021, 11, 1564. Lee, S.; Yu, H.; Yang, H.; Song, I.; Choi, J.; Yang, J.; Lim, G.; Kim, K.-S.; Choi, B.; Kwon, J. A Study on Deep Learning Application of Vibration Data and Visualization of Defects for Predictive Maintenance of Gravity Acceleration Equipment. Appl. Sci. 2021, 11, 1564.

Journal reference: Appl. Sci. 2021, 11, 1564
DOI: 10.3390/app11041564

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 hy-pergravity 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 accel-erometer 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 ex-perimental 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.

Subject Areas

artificial intelligence, deep learning, classification model, hyper-gravity machine, vibration monitoring

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