  In industrial applications, nearly half the failures of motors are caused by
the degradation of rolling element bearings (REBs). Therefore, accurately
estimating the remaining useful life (RUL) for REBs are of crucial importance
to ensure the reliability and safety of mechanical systems. To tackle this
challenge, model-based approaches are often limited by the complexity of
mathematical modeling. Conventional data-driven approaches, on the other hand,
require massive efforts to extract the degradation features and construct
health index. In this paper, a novel online data-driven framework is proposed
to exploit the adoption of deep convolutional neural networks (CNN) in
predicting the RUL of bearings. More concretely, the raw vibrations of training
bearings are first processed using the Hilbert-Huang transform (HHT) and a
novel nonlinear degradation indicator is constructed as the label for learning.
The CNN is then employed to identify the hidden pattern between the extracted
degradation indicator and the vibration of training bearings, which makes it
possible to estimate the degradation of the test bearings automatically.
Finally, testing bearings' RULs are predicted by using a $\epsilon$-support
vector regression model. The superior performance of the proposed RUL
estimation framework, compared with the state-of-the-art approaches, is
demonstrated through the experimental results. The generality of the proposed
CNN model is also validated by transferring to bearings undergoing different
operating conditions.
