Utilizing Autoregressive Networks for Full Lifecycle Data Generation of Rolling Bearings for RUL Prediction
This addresses a data scarcity problem for industrial production by generating synthetic vibration data to enhance remaining useful life prediction, representing an incremental improvement through a novel hybrid method.
The paper tackles the scarcity of high-quality, full lifecycle data for rolling bearing lifespan prediction by introducing the CVGAN model, which generates one-dimensional vibration signals conditioned on historical data and remaining useful life, and experiments on the PHM 2012 dataset show it outperforms advanced methods in MMD and FID metrics, significantly improving predictive model performance.
The prediction of rolling bearing lifespan is of significant importance in industrial production. However, the scarcity of high-quality, full lifecycle data has been a major constraint in achieving precise predictions. To address this challenge, this paper introduces the CVGAN model, a novel framework capable of generating one-dimensional vibration signals in both horizontal and vertical directions, conditioned on historical vibration data and remaining useful life. In addition, we propose an autoregressive generation method that can iteratively utilize previously generated vibration information to guide the generation of current signals. The effectiveness of the CVGAN model is validated through experiments conducted on the PHM 2012 dataset. Our findings demonstrate that the CVGAN model, in terms of both MMD and FID metrics, outperforms many advanced methods in both autoregressive and non-autoregressive generation modes. Notably, training using the full lifecycle data generated by the CVGAN model significantly improves the performance of the predictive model. This result highlights the effectiveness of the data generated by CVGans in enhancing the predictive power of these models.