Abstract
The purpose of this study is to highlight approaches for predicting a system’s future behavior and estimating its remaining useful life (RUL) to define an effective maintenance schedule. Indeed, prognosis and health management (PHM) strategies for renewable energy systems, with a focus on wind turbine generators, are given, as well as publications published in the recent ten years. As a result, some prognostic applications in renewable energy systems are emphasized, such as power converter devices, battery capacity degradation, and damage in wind turbine high-speed shaft bearings. The paper not only focuses on the methodologies adopted during the early research in the area of PHM but also investigates more current challenges and trends in this domain
Highlights
RAMS services are widely applied in industrial applications to perform in-depth assessments and interventions
This section explores the methods of prognostics as part of prognosis and health management (PHM)
The instruments used for prognosis are based on the nature of the data obtained and previous knowledge of the system being monitored, while the methods of prognosis are based on the type of intended application
Summary
RAMS (reliability, availability, maintainability, and safety) services are widely applied in industrial applications to perform in-depth assessments and interventions. These metrics were applied to a combination of different algorithms and different datasets. We can use principal component analysis or other fusion techniques to generate a fused HI that incorporates information from more than one HI [2,4,18,19,26,27,28] In this regard, Saxena et al (2009, 2010) [2,18] proposed a set of metrics for evaluating key elements of RUL predictions, including “prognostic horizon (PH)”, “timeliness”, “precision” and “accuracy”, etc. These are measures of difference between the estimated RUL and the actual RUL (see Figures 6 and 7)
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