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RUL Prediction of Deteriorating Products Using an Adaptive Wiener Process Model

机译：使用自适应维纳过程模型的变质产品RUL预测

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摘要

Degradation modeling plays an important role in system health diagnosis and remaining useful life (RUL) prediction. Recently, a class of Wiener process models with adaptive drift was proposed for degradation-based RUL prediction, which has been proven flexible and effective. However, the existing studies use an autoregressive model of order 1 for the adaptive drift, which can result in difficulties in both model estimation and RUL prediction. This paper proposes a new adaptive Wiener process model that utilizes a Brownian motion for the adaptive drift. The new model shares the flexibility of the existing models, but avoids the difficulties in model estimation and RUL prediction. A model estimation procedure based on maximum likelihood estimation is developed, and the RUL prediction based on the proposed model is formulated. The effectiveness of the model in RUL prediction is validated using simulation and through an application to the lithium-ion battery degradation data.
机译：降级建模在系统健康诊断和剩余使用寿命（RUL）预测中起着重要作用。最近，提出了一种具有自适应漂移的Wiener过程模型，用于基于退化的RUL预测，已被证明是灵活有效的。但是，现有的研究将1阶自回归模型用于自适应漂移，这可能导致模型估计和RUL预测都困难。本文提出了一种新的自适应维纳过程模型，该模型利用布朗运动进行自适应漂移。新模型具有现有模型的灵活性，但避免了模型估计和RUL预测方面的困难。提出了基于最大似然估计的模型估计程序，并提出了基于所提出模型的RUL预测。该模型在RUL预测中的有效性通过仿真并通过应用于锂离子电池退化数据得到了验证。

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