LAPSE:2021.0147
Published Article
LAPSE:2021.0147
An Autoencoder Gated Recurrent Unit for Remaining Useful Life Prediction
Yi-Wei Lu, Chia-Yu Hsu, Kuang-Chieh Huang
March 24, 2021
With the development of smart manufacturing, in order to detect abnormal conditions of the equipment, a large number of sensors have been used to record the variables associated with production equipment. This study focuses on the prediction of Remaining Useful Life (RUL). RUL prediction is part of predictive maintenance, which uses the development trend of the machine to predict when the machine will malfunction. High accuracy of RUL prediction not only reduces the consumption of manpower and materials, but also reduces the need for future maintenance. This study focuses on detecting faults as early as possible, before the machine needs to be replaced or repaired, to ensure the reliability of the system. It is difficult to extract meaningful features from sensor data directly. This study proposes a model based on an Autoencoder Gated Recurrent Unit (AE-GRU), in which the Autoencoder (AE) extracts the important features from the raw data and the Gated Recurrent Unit (GRU) selects the information from the sequences to forecast RUL. To evaluate the performance of the proposed AE-GRU model, an aircraft turbofan engine degradation simulation dataset provided by NASA was used and a comparison made of different recurrent neural networks. The results demonstrate that the AE-GRU is better than other recurrent neural networks, such as Long Short-Term Memory (LSTM) and GRU.
Keywords
autoencoder, deep learning, gated recurrent unit, predictive maintenance, remaining useful life
Suggested Citation
Lu YW, Hsu CY, Huang KC. An Autoencoder Gated Recurrent Unit for Remaining Useful Life Prediction. (2021). LAPSE:2021.0147
Author Affiliations
Lu YW: Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei 10608, Taiwan
Hsu CY: Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei 10608, Taiwan
Huang KC: Department of Information Management, Yuan Ze University, Taoyuan 32003, Taiwan
Journal Name
Processes
Volume
8
Issue
9
Article Number
E1155
Year
2020
Publication Date
2020-09-15
Published Version
ISSN
2227-9717
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Original Submission
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PII: pr8091155, Publication Type: Journal Article
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LAPSE:2021.0147
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doi:10.3390/pr8091155
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Mar 24, 2021
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Mar 24, 2021
 
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Original Submitter
Calvin Tsay
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