Journal of System Simulation
Abstract
Abstract: Aiming at the fault prediction problem of a turbofan engine, a fault prediction model based on evidential reasoning (ER) and belief rule base (BRB) is proposed. In order to describe the health state of turbofan engine, ER algorithm is adopted to fuse the state information. Combined with prior knowledge, a hybrid driven simulation prediction of BRB model is established. Projection covariance matrix adaptive evolution strategy (P-CMA-ES) is used to optimize the model parameters. The validity of the model is verified by experiments. Experimental results show that the proposed method not only accurately predicts the probability of failure risk of the turbofan engine, but also provides strong support for fault diagnosis and maintenance support.
Recommended Citation
Zhu, Hailong; Jia, Ruxia; Zhang, Liang; and He, Wei
(2022)
"Turbofan Engine Fault Prediction Based on Evidential Reasoning and Belief Rule Base,"
Journal of System Simulation: Vol. 34:
Iss.
9, Article 17.
DOI: 10.16182/j.issn1004731x.joss.21-0396
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol34/iss9/17
First Page
2074
Revised Date
2021-08-15
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.21-0396
Last Page
2086
CLC
TP182;V263;TP391
Recommended Citation
Hailong Zhu, Ruxia Jia, Liang Zhang, Wei He. Turbofan Engine Fault Prediction Based on Evidential Reasoning and Belief Rule Base[J]. Journal of System Simulation, 2022, 34(09): 2074-2086.
DOI
10.16182/j.issn1004731x.joss.21-0396
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