A Generalized Remaining Useful Life Prediction Method for Complex Systems Based on Composite Health Indicator
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- @Article{WEN:2020:RESS,
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author = "Pengfei Wen and Shuai Zhao and Shaowei Chen and
Yong Li",
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title = "A Generalized Remaining Useful Life Prediction Method
for Complex Systems Based on Composite Health
Indicator",
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journal = "Reliability Engineering \& System Safety",
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pages = "107241",
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year = "2020",
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ISSN = "0951-8320",
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DOI = "doi:10.1016/j.ress.2020.107241",
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URL = "http://www.sciencedirect.com/science/article/pii/S0951832020307419",
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keywords = "genetic algorithms, genetic programming, Multiple
sensors, Data fusion, Degradation modeling, Remaining
useful life, Prognostics",
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abstract = "As one of the key techniques in Prognostics and Health
Management (PHM), accurate Remaining Useful Life (RUL)
prediction can effectively reduce the number of
downtime maintenance and significantly improve economic
benefits. In this paper, a generalized RUL prediction
method is proposed for complex systems with multiple
Condition Monitoring (CM) signals. A stochastic
degradation model is proposed to characterize the
system degradation behavior, based on which the
respective reliability characteristics such as the RUL
and its Confidence Interval (CI) are explicitly
derived. Considering the degradation model, two
desirable properties of the Health Indicator (HI) are
put forward and their respective quantitative
evaluation methods are developed. With the desirable
properties, a nonlinear data fusion method based on
Genetic Programming (GP) is proposed to construct a
superior composite HI. In this way, the multiple CM
signals are fused to provide a better prediction
capability. Finally, the proposed integrated
methodology is validated on the C-MAPSS data set of
aircraft turbine engines",
- }
Genetic Programming entries for
Pengfei Wen
Shuai Zhao
Shaowei Chen
Yong Li
Citations