Abstract The reliability data are very useful in maintenance operations since they are used to calculate the Mean Time To Failure. However, they are rarely used for the online calculation of the Remaining Useful Life (RUL). This is because the features measured in the reliability tests are not always measurable online. In this paper, the proposed solution is usage of physical knowledge of the components to build models linking these features to the variables measurable online. Then, the physical models are used to generate health indices whose evolution can be estimated and predicted online, and the reliability data used for initializing the trend models of the health indices. To guarantee the robustness of the remaining useful life estimation to changes in Condition Monitoring, this paper proposes to update online the drift parameter of Wiener process. The latter is used to model the trend of the health indices. The results obtained by the most used updating methods in the literature are analyzed and compared in order to highlight the influence of the model updating on prognosis performance. Experimental results, obtained by an application to estimate the RUL of MOS Field-Effect Transistor, show the effectiveness of the proposed method.

中文翻译：

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基于物理知识和可靠性数据的故障诊断和预测：在MOS场效应晶体管中的应用

摘要 可靠性数据在维护操作中非常有用，因为它们用于计算平均无故障时间。但是，它们很少用于在线计算剩余使用寿命 (RUL)。这是因为可靠性测试中测量的特征并不总是可以在线测量的。在本文中，提出的解决方案是使用组件的物理知识来构建模型，将这些特征与可在线测量的变量联系起来。然后，利用物理模型生成可以在线估计和预测其演变的健康指标，并使用可靠性数据来初始化健康指标的趋势模型。为了保证剩余使用寿命估计对状态监测变化的稳健性，本文提出在线更新维纳过程的漂移参数。后者用于对健康指数的趋势进行建模。对文献中最常用的更新方法获得的结果进行了分析和比较，以突出模型更新对预后性能的影响。通过估计 MOS 场效应晶体管 RUL 的应用程序获得的实验结果表明了所提出方法的有效性。