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Component based Data-driven Prognostics for Complex Systems: Methodology and Applications

机译：基于组件的复杂系统数据驱动的预测：方法和应用程序

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摘要

In recent years, considerable research efforts have been applied in the field of fault prognostics. However, to the authors knowledge, there are few published works that address complete and systematic methods describing the steps required to develop data-driven prognostics approaches for complex systems. This paper presents a generic component-based prognostics methodology that can be customized for different applications and which can be useful for new researchers and engineers. The paper is divided into two parts. The first part provides a description of the procedures required before constructing data-driven prognostics, such as identifying critical components, selecting physical parameters to monitor, choosing monitoring sensors and defining the data acquisition system. The second part presents a novel data-driven prognostic method for direct remaining useful life (RUL) prediction. This method relies on two phases: offline and online. In the offline phase, a method for constructing health indicators (HI) from sensor data is presented. Such HIs can be used as offline models to display the deterioration evolution of components over time. In the online phase, similar HIs are constructed from the sensor data for a new component. Then, a discrete Bayesian filter is applied to estimate the current health status. Finally, the offline database is searched to find the closest group to the online His. The selected offline HIs can be used for estimating the RUL of the new component under operation. The performance of the method is demonstrated using two real data sets taken from the NASA Ames prognostics data repository.
机译：近年来，大量的研究工作已经在故障预测领域应用。然而，据作者所知，很少有发表作品是描述的步骤地址完整和系统的方法需要开发数据驱动的预后方法的复杂系统。本文提出了一种通用的基于组件的预诊断方法，它可以定制不同的应用，并且可以成为新的研究人员和工程师是有用的。本文分为两部分。第一部分提供的构建数据驱动预后，如识别关键部件，在选择物理参数，显示器，选择监测传感器并限定数据采集系统之前所需的程序的描述。第二部分介绍用于直接剩余使用寿命（RUL）预测的新的数据驱动的预后方法。这种方法依赖于两个阶段：离线和在线。在脱机阶段，提出了一种用于从传感器数据构造健康指标（HI）的方法。这样他可以作为离线模式随着时间的推移，以显示组件的恶化演进。在联机阶段，类似于他的从传感器数据构造为一个新的组件。然后，离散贝叶斯滤波器应用于估计当前的健康状况。最后，离线数据库进行搜索，以他在网上找到最接近组到。所选择的离线他的可以用于操作下估计所述新组件的RUL。该方法的性能，使用来自NASA埃姆斯预后数据储存库提取的两个真实数据组证实。

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