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首页> 外文期刊>Mechanical systems and signal processing >An improved Wiener process model with adaptive drift and diffusion for online remaining useful life prediction
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An improved Wiener process model with adaptive drift and diffusion for online remaining useful life prediction

机译：带有自适应漂移和扩散的改进的维纳过程模型，用于在线剩余使用寿命预测

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摘要

Remaining useful life (RUL) prediction plays an important role in the field of prognostics and health management (PHM). Although several Wiener process models with adaptive drift have been developed for RUL prediction, these models assume the diffusion parameter is fixed and therefore fail to capture the real degradation process. Accordingly, this paper proposes an improved Wiener process model for RUL prediction, in which both drift and diffusion parameters are adaptive with the updating of monitoring data. The proposed model considers the quantitative relationship between degradation rate and degradation variation. When a new monitoring data is available, we update the model parameters and therefore the RUL distribution by applying recursive filter and expectation maximization (EM) algorithm. In addition, a prediction region is constructed based on the 3 sigma-interval criterion to eliminate the abnormal monitoring data, followed by a model selection method developed to compare the prediction accuracy of the proposed model with the existing models. The proposed model's superiority and the effectiveness of the model selection method are illustrated and validated by an application to the identical thrust ball bearings. (C) 2019 Elsevier Ltd. All rights reserved.
机译：剩余使用寿命（RUL）预测在预后和健康管理（PHM）领域中起着重要作用。尽管已经为RUL预测开发了几种具有自适应漂移的Wiener过程模型，但是这些模型假定扩散参数是固定的，因此无法捕获实际的退化过程。因此，本文提出了一种改进的用于RUL预测的Wiener过程模型，其中漂移和扩散参数都与监视数据的更新相适应。提出的模型考虑了降解率和降解变化之间的定量关系。当有新的监视数据可用时，我们通过应用递归滤波器和期望最大化（EM）算法来更新模型参数，从而更新RUL分布。此外，基于3 sigma-interval准则构造一个预测区域以消除异常监视数据，然后开发一种模型选择方法以将所提出模型的预测精度与现有模型进行比较。通过对相同的推力球轴承的应用，说明并验证了所提出模型的优越性和模型选择方法的有效性。 （C）2019 Elsevier Ltd.保留所有权利。

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