Open AccessAeroengine Performance Degradation Evaluation Method Based on Hierarchical Bayes Integrated with DNN Fusion Decision
Author(s) -
Yongbo Li,
Huawei Wang,
Qiang Fu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1910/1/012001
Subject(s) - bayes' theorem , artificial neural network , reliability (semiconductor) , robustness (evolution) , degradation (telecommunications) , computer science , reliability engineering , noise (video) , artificial intelligence , redundancy (engineering) , machine learning , engineering , bayesian probability , telecommunications , power (physics) , biochemistry , physics , chemistry , quantum mechanics , image (mathematics) , gene
According to the performance degradation law of aeroengine in operation, we proposed a method of aeroengine performance degradation evaluation based on the fusion decision of Hierarchical Bayes method (HB) and deep neural network (DNN). Hierarchical Bayes method was used to build the aeroengine reliability model. Then Based on the aeroengine operation monitoring parameters and reliability model, we used deep learning method to extract the performance degradation law and to evaluate aeroengine performance level, realized the fusion decision of engine performance degradation assessment. 9467 monitoring samples, contaminated with sensor noise, collected from 50 aeroengines was used to evaluate the quality of the network, and the average accuracy is 92.01%. The results showed that this method showed good robustness and reduced the risk of the evaluation results error caused by noise.