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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Veröffentlicht in:IEEE transactions on industrial electronics (1982) Jg. 65; H. 2; S. 1539 - 1548
Hauptverfasser: Zhao, Rui, Wang, Dongzhe, Yan, Ruqiang, Mao, Kezhi, Shen, Fei, Wang, Jinjiang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.02.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Computational modeling
Data mining
Downtime
Failure
Fault detection
Fault diagnosis
feature engineering
Feature extraction
gated recurrent unit (GRU)
Gearboxes
Health
Logic gates
machine health monitoring (MHM)
Machine learning
Monitoring
Predictive maintenance
Representations
Sensors
Tool wear
tool wear prediction
Windows (intervals)
ISSN:0278-0046, 1557-9948
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2017.2733438