Apply VGGNet-based deep learning model of vibration data for prediction model of gravity acceleration equipment
This work addresses safety and cost issues in hypergravity equipment maintenance, but it is incremental as it applies an existing deep learning method to a new domain-specific dataset.
The paper tackled failure prediction in hypergravity accelerators by converting vibration signals to spectrograms and using a VGGNet-based deep learning model for classification, achieving a 99.5% F1-Score, which is 23% higher than existing feature-based models.
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.