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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Published in:	Scientific reports Vol. 12; no. 1; pp. 6491 - 14
Main Authors:	Peng, Cheng, Chen, Yufeng, Gui, Weihua, Tang, Zhaohui, Li, Changyun
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 20.04.2022 Nature Publishing Group Nature Portfolio
Subjects:	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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Abstract	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.
AbstractList	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. Abstract 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. 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.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.
ArticleNumber	6491
Author	Gui, Weihua Chen, Yufeng Tang, Zhaohui Li, Changyun Peng, Cheng
Author_xml	– sequence: 1 givenname: Cheng surname: Peng fullname: Peng, Cheng email: chengpeng@csu.edu.cn organization: Hunan University of Technology, Central South University – sequence: 2 givenname: Yufeng surname: Chen fullname: Chen, Yufeng organization: Hunan University of Technology – sequence: 3 givenname: Weihua surname: Gui fullname: Gui, Weihua organization: Central South University – sequence: 4 givenname: Zhaohui surname: Tang fullname: Tang, Zhaohui organization: Central South University – sequence: 5 givenname: Changyun surname: Li fullname: Li, Changyun organization: Hunan University of Technology
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Snippet	In turbofan engine datasets, to address problems, such as noise interference, diverse data types, large data volumes, complex feature extraction, inability to... Abstract In turbofan engine datasets, to address problems, such as noise interference, diverse data types, large data volumes, complex feature extraction,...
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StartPage	6491
SubjectTerms	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
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Title	Remaining useful life prognosis of turbofan engines based on deep feature extraction and fusion
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Volume	12
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