Remaining useful life prognosis of turbofan engines based on deep feature extraction and fusion

In turbofan engine datasets, to address problems, such as noise interference, diverse data types, large data volumes, complex feature extraction, inability to effectively describe degradation trends, and poor remaining useful life (RUL) prognosis effects, a remaining useful life prognosis model comb...

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Vydáno v:Scientific reports Ročník 12; číslo 1; s. 6491 - 14
Hlavní autoři: Peng, Cheng, Chen, Yufeng, Gui, Weihua, Tang, Zhaohui, Li, Changyun
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 20.04.2022
Nature Publishing Group
Nature Portfolio
Témata:
639/166/988
639/705/258
Accuracy
Aircraft
Algorithms
Computer Simulation
Crack propagation
Datasets
Deep learning
Engines
Experiments
Gene Fusion
Humanities and Social Sciences
Methods
multidisciplinary
Neural networks
Noise
Optimization
Prediction models
Prognosis
Science
Science (multidisciplinary)
Useful life
ISSN:2045-2322, 2045-2322
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Popis
Shrnutí:In turbofan engine datasets, to address problems, such as noise interference, diverse data types, large data volumes, complex feature extraction, inability to effectively describe degradation trends, and poor remaining useful life (RUL) prognosis effects, a remaining useful life prognosis model combining an improved stack sparse autoencoder (imSSAE) and an improved echo state network (imESN) is proposed in this paper. First, the 3-sigma criterion is adopted to remove the noise and reconstruct the data, and then the deep features of the engine are extracted by using an imSSAE and fused into health indicator (HI) curves describing the engine degradation trend. Finally, an attention mechanism is introduced into an imESN to adaptively process different types of data and obtain the RUL. The experimental results based on the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset show that compared with the other popular RUL prediction models, the combined model proposed in this paper has higher prediction accuracy, and the evaluation indices also show the effectiveness and superiority of the model.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-022-10191-2