Bearing Remaining Useful Life Prediction Based on AdCNN and CWGAN under Few Samples

Abstract

At present, deep learning is widely used to predict the remaining useful life (RUL) of rotation machinery in failure prediction and health management (PHM). However, in the actual manufacturing process, massive rotating machinery data are not easily obtained, which will lead to the decline of the prediction accuracy of the data-driven deep learning method. Firstly, a novel prognostic framework is proposed, which is comprised of conditional Wasserstein distance-based generative adversarial networks (CWGAN) and adversarial convolution neural networks (AdCNN), which can stably generate high-quality training samples to augment the bearing degradation dataset and solve the problem of few samples. Then, the bearing RUL prediction method is realized by inputting the monitoring data into the one-dimensional convolutional neural network (1DCNN) for adversarial training. Via the bearing degradation dataset of the IEEE 2012 PHM data challenge, the reliability of the proposed method is verified. Finally, experimental results show that our approach is better than others in RUL prediction on average absolute deviation and average square root error.