Remaining Useful Life Prediction of IIoT-Enabled Complex Industrial Systems With Hybrid Fusion of Multiple Information Sources
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gp-bibliography.bib Revision:1.8081
- @Article{Wen:2021:IoT,
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author = "Pengfei Wen and Yong Li and Shaowei Chen and
Shuai Zhao",
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title = "Remaining Useful Life Prediction of IIoT-Enabled
Complex Industrial Systems With Hybrid Fusion of
Multiple Information Sources",
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journal = "IEEE Internet of Things Journal",
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year = "2021",
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volume = "8",
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number = "11",
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pages = "9045--9058",
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abstract = "Industrial Internet of Things has significantly
boosted predictive maintenance for complex industrial
systems, where the accurate prediction of remaining
useful life (RUL) with high-level confidence is
challenging. By aggregating multiple informative
sources of system degradation, information fusion can
be applied to improve the prediction accuracy and
reduce the uncertainty. It can be performed on the
data-level, feature-level, and decision-level. To fully
exploit the available degradation information, this
article proposes a hybrid fusion method on both the
data level and decision level to predict the RUL. On
the data level, genetic programming (GP) is adopted to
integrate physical sensor sources into a composite
health indicator (HI), resulting in an explicit
nonlinear data-level fusion model. Subsequently, the
predictions of the RUL based on each physical sensor
and the developed composite HI are synthesized in the
framework of belief functions theory, as the
decision-level fusion method. Moreover, the
decision-level method is flexible for incorporating
other statistical data-driven methods with explicit
estimations of the RUL. The proposed method is verified
via a case study on NASA's C-MAPSS data set. Compared
to the single-level fusion methods, the results confirm
the superiority of the proposed method for higher
accuracy and certainty of predicting the RUL.",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/JIOT.2021.3055977",
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ISSN = "2327-4662",
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month = jun,
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notes = "Also known as \cite{9343303}",
- }
Genetic Programming entries for
Pengfei Wen
Yong Li
Shaowei Chen
Shuai Zhao
Citations