Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/140066
Title: Machine health monitoring using local feature-based gated recurrent unit networks
Authors: Zhao, Rui
Wang, Dongzhe
Yan, Ruqiang
Mao, Kezhi
Shen, Fei
Wang, Jinjiang
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2017
Source: 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
Journal: IEEE Transactions on Industrial Electronics
Abstract: 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.
URI: https://hdl.handle.net/10356/140066
ISSN: 0278-0046
DOI: 10.1109/TIE.2017.2733438
Rights: © 2017 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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