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.
翻译:在工业应用中,发动机故障近一半是由滚动轴承(REBs)退化造成的。因此,准确估计REBs剩余使用寿命(RUL)对于确保机械系统的可靠性和安全性至关重要。为了应对这一挑战,基于模型的方法往往受到数学模型复杂程度的限制。常规数据驱动的方法则需要大量的努力来提取降解特征和构建健康指数。在本文中,提议了一个新的在线数据驱动框架,以利用深层革命神经网络(CNN)来预测轴承的RUL。更具体地说,培训轴承的原始振动首先使用HHHT(HHT)来进行处理,并用新的非线性降解指标作为学习的标签。随后,CNN被用来确定提取的降解指标与培训影响之间的隐藏模式,从而可以自动估计测试轴承的退化。最后,测试RULs的测试过程的原始振动是使用$/Eplon的模型,拟议的SIMRU运行模型的升级结果也是通过SICRR的升级模型预测的。拟议的递算结果的升级模型。拟议的递增后结果是SU。拟议的递制。