基于深度学习的轴承使用寿命的预测研究

Research on Bearing Service Life Prediction Based on Deep Learning

摘要: 本文依据PHM2012轴承退化数据集，运用CNN神经网络、LSTM神经网络和CNN + LSTM神经网络分别对轴承的剩余使用寿命进行研究预测，研究表明CNN + LSTM的结合神经网络更能超前预测滚动轴承的剩余使用寿命，降低轴承故障带来的影响。

Abstract: Based on the PHM2012 bearing degradation data set, this paper uses CNN neural network, LSTM neural network and CNN + LSTM neural network to predict the remaining service life of bearings respectively. The research shows that the combined neural network of CNN + LSTM can predict the remaining service life of rolling bearings in advance, and reduce the impact of bearing failure.