Symbolic Regression for Fault Prognosis and Remaining Useful Life Estimation*
Created by W.Langdon from
gp-bibliography.bib Revision:1.8129
- @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