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Hypergravity accelerators are a type of large machinery used for gravitytraining or medical research. A failure of such large equipment can be aserious problem in terms of safety or costs. This paper proposes a predictionmodel that can proactively prevent failures that may occur in a hypergravityaccelerator. The method proposed in this paper was to convert vibration signalsto spectograms and perform classification training using a deep learning model.An experiment was conducted to evaluate the performance of the method proposedin this paper. A 4-channel accelerometer was attached to the bearing housing,which is a rotor, and time-amplitude data were obtained from the measuredvalues by sampling. The data were converted to a two-dimensional spectrogram,and classification training was performed using a deep learning model for fourconditions of the equipment: Unbalance, Misalignment, Shaft Rubbing, andNormal. 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-basedlearning models.

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