Abstract
Bearing is one of the important components in the mechanical system, and the reliability of the bearing is significant. The paper aimed to simulate remaining useful life (RUL) prediction process in the engineering project. In the process of predicting the RUL of the bearing, the noisy in the data leads to the over-fitting phenomenon. In order to solve the problem, it is necessary to train a Variational Auto-Encoder (VAE) for denoising short time series before the prediction starts. In the paper, the VAE was selected to denoise the bearing performance degradation index sequence. The denoised data curve was better than the original, which was smooth. The denoised data were used to solve the bearing RUL prediction problem based on LSTM network, and the over-fitting phenomenon did not occur. Compared with the prediction results of directly using Support Vector machine Regression (SVR) and Artificial Neural Network (ANN), the deviation of the prediction based on Long Short-term Memory (LSTM) results from the observation values were smaller, and the method can be applied in the engineering project.
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Wang, X., Guo, J., Wang, J., Liu, C., Du, C. (2021). Prediction of Bearing Remaining Useful Life Based on LSTM Network. In: Xu, J., Pandey, K.M. (eds) Mechanical Engineering and Materials. Mechanisms and Machine Science, vol 100. Springer, Cham. https://doi.org/10.1007/978-3-030-68303-0_7
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DOI: https://doi.org/10.1007/978-3-030-68303-0_7
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