HYGP-MSAM based model for slewing bearing residual useful life prediction
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{DING:2019:Measurement,
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author = "Peng Ding and Hua Wang and Weigang Bao and
Rongjing Hong",
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title = "{HYGP-MSAM} based model for slewing bearing residual
useful life prediction",
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journal = "Measurement",
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volume = "141",
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pages = "162--175",
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year = "2019",
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ISSN = "0263-2241",
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DOI = "doi:10.1016/j.measurement.2019.04.039",
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URL = "http://www.sciencedirect.com/science/article/pii/S0263224119303574",
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keywords = "genetic algorithms, genetic programming, Slewing
bearing, Life model expression, Symbolic regression,
Condition monitoring and life prediction, Coupling
between signals",
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abstract = "Slewing bearings are critical functional components of
large machinery and their residual useful life (RUL)
prediction can avoid downtime and reduce accidents and
casualties. In the field of their condition monitoring
and life prediction, multi-signal and multi-feature
fusion (MSMFF) is a trend for over the current
literatures. However, most of the existing researches
only consider the independent effect of degradation
indicators, thereby ignoring the coupling effect
between different signals. To overcome this gap and
further compensate for the lacks of transparency and
practical meaning in data-driven approaches, especially
for artificial intelligence ones, this paper proposes
an adaptive symbolic regression based modeling
strategy: hybrid genetic programming-model structure
adaptive method (HYGP-MSAM), integrating the strengths
of HYGP algorithm which is a realization based on
symbolic regression directly obtaining explicit
analytical expressions for the life model compared with
{"}black box{"} modeling methods and MSAM aiming for
reconstructing the initial models with coupling terms.
To get better description of degradation trend,
ensemble empirical mode decomposition combined with
singular value decomposition (EEMD-SVD) denoising
method is employed for raw signals and degradation
indicators are obtained through a manifold learning
based fusion algorithm. The proposed HYGP-MSAM modeling
strategy is used to establish life model expressions
afterwards. Finally, life models in the form of
function expressions are derived and an accelerated
run-to-failed experiment is carried out to test this
strategy. It is shown that adaptive coupling
reconstruction strategy for upgrading the symbolic
regression based modeling methods can greatly improve
the fault tolerance of algorithms under parametric
error and effectively improve the prediction accuracy",
- }
Genetic Programming entries for
Peng Ding
Hua Wang
Weigang Bao
Rongjing Hong
Citations