# A deep learning-based remaining useful life prediction approach for bearings

## TL;DR

This paper introduces a deep learning framework using CNNs and Hilbert-Huang transform for accurate, online prediction of remaining useful life in bearings, improving reliability in industrial systems.

## Contribution

It presents a novel CNN-based method combined with HHT for automatic RUL prediction, reducing feature extraction efforts and enhancing adaptability across conditions.

## Key findings

- Outperforms existing RUL prediction methods in experiments.
- Successfully transfers across different bearing operating conditions.
- Demonstrates high accuracy and robustness in RUL estimation.

## Abstract

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.