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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Main Authors: Zhao, Rui, Wang, Dongzhe, Yan, Ruqiang, Mao, Kezhi, Shen, Fei, Wang, Jinjiang
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2020
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Online Access:https://hdl.handle.net/10356/140066
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Institution: Nanyang Technological University
Language: English
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spelling 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.
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
Fault Diagnosis
Feature Engineering
spellingShingle 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
description 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.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Zhao, Rui
Wang, Dongzhe
Yan, Ruqiang
Mao, Kezhi
Shen, Fei
Wang, Jinjiang
format 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
publishDate 2020
url https://hdl.handle.net/10356/140066
_version_ 1681059433276243968