Probabilistic forecasting informed failure prognostics framework for improved RUL prediction under uncertainty: A transformer case study

Aizpurua J.I.; Muxika E.; Penalba M.Stewart B.G.McArthur S.D.J.Barrenetxea M.Ringwood J.V.

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Probabilistic forecasting informed failure prognostics framework for improved RUL prediction under uncertainty: A transformer case study

机译：Probabilistic forecasting informed failure prognostics framework for improved RUL prediction under uncertainty: A transformer case study

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? 2022 Elsevier LtdThe energy transition towards resilient and sustainable power plants requires moving from periodic health assessment to condition-based lifetime planning, which in turn, creates new challenges and opportunities for health estimation and prediction. Probabilistic forecasting models are being widely employed to predict the likely evolution of power grid parameters, such as weather prediction models and probabilistic load forecasting models, that precisely impact on the health state of power and energy components. These models synthesize forecasting knowledge and associated uncertainty information, and their integration within asset management practice would improve lifetime estimation under uncertainty through uncertainty-aware probabilistic predictions. Accordingly, this paper presents a probabilistic prognostics method for lifetime planning under uncertainty integrating data-driven probabilistic forecasting models with expert-knowledge based Bayesian filtering methods. The proposed concepts are applied and validated with power transformers operated in two different power generation systems and obtained results confirm that the proposed probabilistic transformer lifetime estimate aids in the decision-making process with informative lifetime distributions and associated confidence intervals.

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来源
《Reliability engineering & system safety》 |2022年第10期|108676.1-108676.13|共13页
作者
Aizpurua J.I.; Muxika E.; Penalba M.Stewart B.G.McArthur S.D.J.Barrenetxea M.Ringwood J.V.;
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作者单位

Mondragon University Electronics & Computer Science Department - Signal Theory & CommunicationsMondragon University Electronics & Computer Science Department - Signal Theory & Communications||Ikerbasque Basque Foundation for Science;

||Mondragon University Electronics & Computer Science Department - Signal Theory & Communications;

Ikerbasque Basque Foundation for Science||Mondragon University Mechanical & Industrial Production Department - Fluid MechanicsUniversity of Strathclyde Department of Electronic and Electrical Engineering - Institute for Energy & EnvironmentMondragon University Electronics & Computer Science Department - Power ElectronicsMaynooth University Electronic Engineering Department;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种英语
中图分类
关键词
Condition monitoring; Probabilistic forecasting; Prognostics; Transformer; Uncertainty;

Probabilistic forecasting informed failure prognostics framework for improved RUL prediction under uncertainty: A transformer case study

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