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首页> 外文会议>BMEI 2012;International Conference on Biomedical Engineering and Informatics >Remaining useful life prognosis of bearing based on Gauss process regression
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Remaining useful life prognosis of bearing based on Gauss process regression

机译：基于高斯过程回归的轴承剩余使用寿命预测

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Remaining useful life (RUL) prognosis of bearing is an enabling step for efficient implementation of condition based maintenance. Through intelligent tracking of features, status of bearing can be monitored and a rough RUL can be calculated. This paper presents an application of an important Bayesian machine learning method named Gaussian Process Regression (GPR) for bearing features tracking. The Gaussian process model can provide variance around its mean prediction to describe associated uncertainty in the evaluation and prediction. In this case, the GPR models with three different kinds of covariance functions are discussed for feature tracking and RUL evaluation. The dynamic model is introduced to realize a better accuracy prognosis of the bearing RUL by analyzing two important features. The experimental results show that using GPR for prognosis can achieve a high accuracy. In addition, the comparisons of prediction with different type of training covariance functions are discussed. Finally, this method provides a new way for prognosis of fluctuated signal of bearing features.
机译：轴承的剩余使用寿命（RUL）预后是有效实施基于状态的维护的一个使能步骤。通过特征的智能跟踪，可以监视轴承状态并可以计算出粗略的RUL。本文介绍了一种重要的贝叶斯机器学习方法，即高斯过程回归（GPR）在轴承特征跟踪中的应用。高斯过程模型可以提供围绕其均值预测的方差，以描述评估和预测中的相关不确定性。在这种情况下，将讨论具有三种不同协方差函数的GPR模型以进行特征跟踪和RUL评估。通过分析两个重要特征，引入动态模型以实现更好的轴承RUL精度预测。实验结果表明，将GPR用于预后可以达到较高的准确性。此外，还讨论了预测与不同类型的训练协方差函数的比较。最后，该方法为轴承特征波动信号的预测提供了一种新方法。

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