Genetic Programming-Based Feature Construction for System Setting Recognition and Component-Level Prognostics
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{calabrese:2022:AS,
-
author = "Francesca Calabrese and Alberto Regattieri and
Raffaele Piscitelli and Marco Bortolini and
Francesco Gabriele Galizia",
-
title = "Genetic Programming-Based Feature Construction for
System Setting Recognition and Component-Level
Prognostics",
-
journal = "Applied Sciences",
-
year = "2022",
-
volume = "12",
-
number = "9",
-
pages = "Article No. 4749",
-
keywords = "genetic algorithms, genetic programming",
-
ISSN = "2076-3417",
-
URL = "https://www.mdpi.com/2076-3417/12/9/4749",
-
DOI = "doi:10.3390/app12094749",
-
abstract = "Extracting representative feature sets from raw
signals is crucial in Prognostics and Health Management
(PHM) for components’ behaviour understanding.
The literature proposes various methods, including
signal processing in the time, frequency, and
time–frequency domains, feature selection, and
unsupervised feature learning. An emerging task in data
science is Feature Construction (FC), which has the
advantages of both feature selection and feature
learning. In particular, the constructed features
address a specific objective function without requiring
a label during the construction process. Genetic
Programming (GP) is a powerful tool to perform FC in
the PHM context, as it allows to obtain distinct
feature sets depending on the analysis goal, i.e.,
diagnostics and prognostics. This paper adopts GP to
extract system-level features for machinery setting
recognition and component-level features for
prognostics. Three distinct fitness functions are
considered for the GP training, which requires a set of
statistical time-domain features as input. The
methodology is applied to vibration signals extracted
from a test rig during run-to-failure tests under
different settings. The performances of constructed
features are evaluated through the classification
accuracy and the Remaining Useful Life (RUL) prediction
error. Results demonstrate that GP-based features
classify known and novel machinery operating conditions
better than feature selection and learning methods.",
-
notes = "also known as \cite{app12094749}",
- }
Genetic Programming entries for
Francesca Calabrese
Alberto Regattieri
Raffaele Piscitelli
Marco Bortolini
Francesco Gabriele Galizia
Citations