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.
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.
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.
Keywords
artificial intelligence, deep learning, classification model, hyper-gravity machine, vibration monitoring
Subject
Engineering, Automotive Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.