Deep learning application of vibration data for predictive maintenance of gravity acceleration equipment
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Hypergravity accelerators are used for gravity training or medical research. They are a kind of large machinery, and a failure of large equipment can be a serious problem in terms of safety or costs. In this paper, we propose a predictive maintenance model that can proactively prevent failures that may occur in a hypergravity accelerator. The method proposed in this paper is to convert vibration signals into spectograms and perform classification training using a deep learning model. We conducted an experiment to evaluate the performance of the method proposed in this paper. We attached a 4-channel accelerometer to the bearing housing which is a rotor, and obtained time-amplitude data from measured values by sampling. Then, the data was converted into 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. Experimental results showed that the proposed method has an accuracy of 99.5 compared to existing feature-based learning models.
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