Symbolic Regression for Fault Prognosis and Remaining Useful Life Estimation*

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@InProceedings{Safikou:2023:ACC,

• author = "Efi Safikou and George M. Bollas",
• booktitle = "2023 American Control Conference (ACC)",
• title = "Symbolic Regression for Fault Prognosis and Remaining Useful Life Estimation*",
• year = "2023",
• pages = "4715--4720",
• month = may,
• keywords = "genetic algorithms, genetic programming, Degradation, Heating systems, Analytical models, Heuristic algorithms, Life estimation, Predictive models, Systems modelling",
• DOI = "doi:10.23919/ACC55779.2023.10156572",
• ISSN = "2378-5861",
• abstract = "We present a hybrid scheme for prognostics and system health management, which combines system modelling methods and regression-based approaches. Along these lines, we perform parameter trending using symbolic regression, by implementing a genetic programming algorithm that integrates the system model based on the available sensor data. The obtained fault function is an analytical expression for the progression of the system fault in time, which provides valuable insights on its causality. For comparison purposes, we also employ a dynamic degradation regression model that encompasses as health indicators inferential sensors that have been optimised by combining symbolic regression and information theory. To highlight the effectiveness of the proposed framework, both of the aforementioned approaches are applied to a dynamic model of a cross-flow plate-fin heat exchanger toward predicting fault occurrences and estimating the remaining useful life of the system, for various levels of measurement noise.",
• notes = "Also known as \cite{10156572}",

}

Genetic Programming entries for Efstathia Safikou George M Bollas

Citations