Data Amplification for Bearing Remaining Useful Life Prediction Based on Generative Adversarial Network
Luyao Lei, Xiang Li, Juan Wen, Junhao Miao · Wireless Communications and Mobile Computing · 2022
To deal with the difficulty in bearing remaining useful life prediction caused by the lack of history data, a data amplification method based on the generative adversarial network (GAN) is proposed in this paper, and the parameters of generator and discriminator in the GAN are determined by grid search algorithm. The proposed method is verified by the XJTU‐SY bearing data sets from Xi’an Jiaotong University. First, 15 time‐domain features related to the bearing life are extracted as the training data of the GAN to generate virtual data that can be used to build bearing life prediction models. Then, support vector regression and the radial basis function neural network are used to construct the bearing prognostic model based on real data, generated data, and mixed data. The results show that the proposed method can make up for the deficiency of data and improve the accuracy of bearing remaining useful life prediction.