Railway turnout system RUL prediction based on feature fusion and genetic programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8051
- @Article{CHEN:2020:Measurement,
-
author = "Cong Chen and Tianhua Xu and Guang Wang and Bo Li",
-
title = "Railway turnout system {RUL} prediction based on
feature fusion and genetic programming",
-
journal = "Measurement",
-
volume = "151",
-
pages = "107162",
-
year = "2020",
-
ISSN = "0263-2241",
-
DOI = "doi:10.1016/j.measurement.2019.107162",
-
URL = "http://www.sciencedirect.com/science/article/pii/S0263224119310280",
-
keywords = "genetic algorithms, genetic programming, RUL
prediction, Railway turnout system, Feature fusion",
-
abstract = "The remaining useful life (RUL) prediction of railway
turnout systems (RTS) is very important to avoid
unplanned shutdowns and reduce labor costs for the
normal operation of railways. One key challenge on RUL
prediction is how to construct an appropriate health
indicator (HI) that can be used to infer conditions of
RTS. Existing methods usually adopt some inherit merits
(e.g., monotonicity, trendability, and robustness), and
their prediction results lack real-world physical
meaning due to their {"}black-box-like{"} property. In
this paper, we present a novel feature fusion method
for RUL prediction, which is able to capture the
relationship between RUL and HI. A variant
correlation-based feature selection method is used to
extract features, which has the potential to depict the
degradation process optimally, and then the selected
features are fused by Auto-Associative Kernel
Regression (AAKR) for prediction. To reduce the noise
interference, the extracted features and the combined
HI are all smoothed by using the locally weighted
regression. Finally, a genetic programming (GP)
algorithm is employed to predict the RUL of RTS. The
proposed method is extensively tested on two turnout
machine degradation datasets, and the results show that
the proposed approach is effective for RUL prediction
of RTS",
-
notes = "State Key Lab of Rail Traffic Control and Safety,
Beijing Jiaotong University, Beijing, China",
- }
Genetic Programming entries for
Cong Chen
Tianhua Xu
Guang Wang
Bo Li
Citations