  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.
