  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.
