Machine health monitoring using local feature-based gated recurrent unit networks
In modern industries, machine health monitoring systems (MHMS) have been applied wildly with the goal of realizing predictive maintenance including failures tracking, downtime reduction, and assets preservation. In the era of big machinery data, data-driven MHMS have achieved remarkable results in t...
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sg-ntu-dr.10356-1400662020-05-26T06:12:39Z Machine health monitoring using local feature-based gated recurrent unit networks Zhao, Rui Wang, Dongzhe Yan, Ruqiang Mao, Kezhi Shen, Fei Wang, Jinjiang School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Fault Diagnosis Feature Engineering In modern industries, machine health monitoring systems (MHMS) have been applied wildly with the goal of realizing predictive maintenance including failures tracking, downtime reduction, and assets preservation. In the era of big machinery data, data-driven MHMS have achieved remarkable results in the detection of faults after the occurrence of certain failures (diagnosis) and prediction of the future working conditions and the remaining useful life (prognosis). The numerical representation for raw sensory data is the key stone for various successful MHMS. Conventional methods are the labor-extensive as they usually depend on handcrafted features, which require expert knowledge. Inspired by the success of deep learning methods that redefine representation learning from raw data, we propose local feature-based gated recurrent unit (LFGRU) networks. It is a hybrid approach that combines handcrafted feature design with automatic feature learning for machine health monitoring. First, features from windows of input time series are extracted. Then, an enhanced bidirectional GRU network is designed and applied on the generated sequence of local features to learn the representation. A supervised learning layer is finally trained to predict machine condition. Experiments on three machine health monitoring tasks: tool wear prediction, gearbox fault diagnosis, and incipient bearing fault detection verify the effectiveness and generalization of the proposed LFGRU. 2020-05-26T06:12:39Z 2020-05-26T06:12:39Z 2017 Journal Article Zhao, R., Wang, D., Yan, R., Mao, K., Shen, F., & Wang, J. (2018). Machine health monitoring using local feature-based gated recurrent unit networks. IEEE Transactions on Industrial Electronics, 65(2), 1539-1548. doi:10.1109/TIE.2017.2733438 0278-0046 https://hdl.handle.net/10356/140066 10.1109/TIE.2017.2733438 2-s2.0-85028972254 2 65 1539 1548 en IEEE Transactions on Industrial Electronics © 2017 IEEE. All rights reserved. |
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Engineering::Electrical and electronic engineering Fault Diagnosis Feature Engineering Zhao, Rui Wang, Dongzhe Yan, Ruqiang Mao, Kezhi Shen, Fei Wang, Jinjiang Machine health monitoring using local feature-based gated recurrent unit networks |
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In modern industries, machine health monitoring systems (MHMS) have been applied wildly with the goal of realizing predictive maintenance including failures tracking, downtime reduction, and assets preservation. In the era of big machinery data, data-driven MHMS have achieved remarkable results in the detection of faults after the occurrence of certain failures (diagnosis) and prediction of the future working conditions and the remaining useful life (prognosis). The numerical representation for raw sensory data is the key stone for various successful MHMS. Conventional methods are the labor-extensive as they usually depend on handcrafted features, which require expert knowledge. Inspired by the success of deep learning methods that redefine representation learning from raw data, we propose local feature-based gated recurrent unit (LFGRU) networks. It is a hybrid approach that combines handcrafted feature design with automatic feature learning for machine health monitoring. First, features from windows of input time series are extracted. Then, an enhanced bidirectional GRU network is designed and applied on the generated sequence of local features to learn the representation. A supervised learning layer is finally trained to predict machine condition. Experiments on three machine health monitoring tasks: tool wear prediction, gearbox fault diagnosis, and incipient bearing fault detection verify the effectiveness and generalization of the proposed LFGRU. |
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School of Electrical and Electronic Engineering |
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School of Electrical and Electronic Engineering Zhao, Rui Wang, Dongzhe Yan, Ruqiang Mao, Kezhi Shen, Fei Wang, Jinjiang |
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Article |
author |
Zhao, Rui Wang, Dongzhe Yan, Ruqiang Mao, Kezhi Shen, Fei Wang, Jinjiang |
author_sort |
Zhao, Rui |
title |
Machine health monitoring using local feature-based gated recurrent unit networks |
title_short |
Machine health monitoring using local feature-based gated recurrent unit networks |
title_full |
Machine health monitoring using local feature-based gated recurrent unit networks |
title_fullStr |
Machine health monitoring using local feature-based gated recurrent unit networks |
title_full_unstemmed |
Machine health monitoring using local feature-based gated recurrent unit networks |
title_sort |
machine health monitoring using local feature-based gated recurrent unit networks |
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2020 |
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https://hdl.handle.net/10356/140066 |
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1681059433276243968 |