A Novel Bayesian-Optimization-Based Adversarial TCN for RUL Prediction of Bearings
Qian Chen, Yi-Ben Liu, Ming‐Feng Ge, Jie Liu, Leimin Wang
Abstract
The methods for remaining useful life (RUL) prediction of bearings are mainly based on the autoregressive strategies, among which the temporal convolutional network (TCN) has been recently developed and is widely believed as the high-performance one. These methods generally suffer from errors of prediction. In this article, we newly design the Bayesian-optimization-based adversarial TCN (AdTCN-BO), by embedding the TCN into the adversarial training framework as the generator. Within the framework, the discriminator is designed to continuously correct the output value of the generator in the training process, thus reducing the errors of prediction to a certain extent. Based on the AdTCN-BO, a novel RUL prediction approach for bearings is developed. An experimental verification is carried out to validate the effectiveness of the proposed approach, demonstrating that the AdTCN-BO framework is more accurate in contrast to the traditional data-driven methods of RUL prediction.