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Physics-based Gaussian process for the health monitoring for a rolling bearing

机译:基于物理的高斯过程,用于滚动轴承的健康监测

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摘要

An improved Gaussian process regression (GPR) is presented to predict the remaining useful life (RUL) of a gyroscope being integral to its prognostics and health management, despite uncertainties in the mean and variance values. In our approach, the degradation model of the gyroscope innovatively serves as a global model in the GPR methodology to capture the actual trends of the RUL. Moreover, the ball bearing whose rolling contact wear is responsible for the drifting in the gyroscope is considered as an essential component for the gyroscope RUL estimation. Employing the GPR method and the physical degradation model, the prognosis for the ball bearing can successfully predicts the defect before it occurs. Compared to other data-driven algorithms, results obtained for a gyroscope in an inertial navigation system confirm that the proposed method can be applied for drift prognostics with significant efficiency.
机译:提出了一种改进的高斯过程回归(GPR),以预测陀螺仪的剩余使用寿命(RUL)是其预测和健康管理的组成部分,尽管均值和方差值存在不确定性。在我们的方法中,陀螺仪的退化模型创新地用作GPR方法中的全局模型,以捕获RUL的实际趋势。此外,其滚动接触磨损是造成陀螺仪漂移的原因的滚珠轴承被认为是陀螺仪RUL估算的必要组成部分。利用GPR方法和物理退化模型,滚珠轴承的预后可以成功地预测缺陷的发生。与其他数据驱动算法相比,惯性导航系统中的陀螺仪获得的结果证实,该方法可有效地用于漂移预测。

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