A Remaining Useful Life Prognosis of Turbofan Engine Using Temporal and Spatial Feature Fusion

A Remaining Useful Life Prognosis of Turbofan Engine Using Temporal and Spatial Feature Fusion

The prognosis of the remaining useful life (RUL) of turbofan engine provides an important basis for predictive maintenance and remanufacturing, and plays a major role in reducing failure rate and maintenance costs. The main problem of traditional methods based on the single neural network of shallow...

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Bibliographic Details
Main Authors: Cheng Peng, Yufeng Chen, Qing Chen, Zhaohui Tang, Lingling Li, Weihua Gui
Format: Article
Language:English
Published: MDPI AG 2021-01-01
Series:Sensors
Subjects:
remaining useful life (RUL)
long short-term memory (LSTM)
one-dimensional convolutional neural networks with full convolutional layer (1-FCLCNN)
temporal and spatial features
turbofan engine
Online Access:https://www.mdpi.com/1424-8220/21/2/418

Internet

https://www.mdpi.com/1424-8220/21/2/418

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