基于VGGNet的振动数据深度学习模型用于重力加速设备的预测模型
Apply VGGNet-based deep learning model of vibration data for prediction model of gravity acceleration equipment
摘要：
超重力加速器是一种用于重力训练或医学研究的大型机械。这种大型设备的故障在安全性或成本方面可能是严重的问题。本文提出了一种预测模型，可以主动预防可能发生的故障，在超重力加速器。本文提出的方法是将振动信号转换为频谱图，并使用深度学习模型进行分类训练。通过实验对本文提出的方法的性能进行了评估。将一个4通道加速度计连接到作为转子的轴承箱上，通过采样从测量值中获得时间-幅度数据。数据被转换为二维频谱图，并使用深度学习模型对设备的四种状态进行分类训练：不平衡，未对准，轴摩擦和正常。实验结果表明，该方法具有99.5%的F1-Score，这是高达23%，比现有的基于特征的学习模型的76.25%。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
