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DeepHealth :
DeepHealth : A Self-Attention Based Method for Instant Intelligent Predictive Maintenance in Industrial Internet of Things
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- Zhang, Weiting (författare)
- Beijing Jiaotong Univ, Sch Elect & Informat Engn, Beijing 100044, Peoples R China.
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- Yang, Dong (författare)
- Beijing Jiaotong Univ, Sch Elect & Informat Engn, Beijing 100044, Peoples R China.
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- Xu, Youzhi (författare)
- Mittuniversitetet,Institutionen för informationssystem och –teknologi
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- Huang, Xuefeng (författare)
- Beijing Sheenline Grp Co Ltd, Beijing 100044, Peoples R China.
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- Zhang, Jun (författare)
- Beijing Sheenline Grp Co Ltd, Beijing 100044, Peoples R China.
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- Gidlund, Mikael, 1972- (författare)
- Mittuniversitetet,Institutionen för informationssystem och –teknologi
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Beijing Jiaotong Univ, Sch Elect & Informat Engn, Beijing 100044, Peoples R China Institutionen för informationssystem och –teknologi (creator_code:org_t)
- 2021
- 2021
- Engelska.
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Ingår i: IEEE Transactions on Industrial Informatics. - 1551-3203 .- 1941-0050. ; 17:8, s. 5461-5473
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- With the rapid development of artificial intelligence and industrial Internet of Things (IIoT) technologies, intelligent predictive maintenance (IPdM) has received considerable attention from researchers and practitioners. To efficiently predict impending failures and mitigate unexpected downtime, while satisfying the instant maintenance demands of industrial facilities is very important for improving the production efficiency. In this article, a self-attention based "Perception and Prediction" framework, called DeepHealth, is proposed for the instant IPdM. Specifically, the framework is composed of two submodels (i.e., DH-1 and DH-2), which are respectively utilized to perform the health perception and sequence prediction. By operating the framework, the proposed models can predict the health conditions via predicting the future signal samples, thereby completing the instant IPdM. Considering the potential temporal correlation in time series, we deploy an enhanced attention mechanism to capture global dependencies from the vibration signals, and leverage the long- and short-term sequence prediction of sensor signals to support instant maintenance decision-making. On this basis, we conduct a destructive experiment based on the IIoT-enabled rotating machinery and construct a balanced industrial dataset for model evaluations. Extensive experiment results show that the proposed solution achieves good prediction accuracy for instant IPdM on the automatic washing equipment and Case Western Reserve University datasets.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)
Nyckelord
- Maintenance engineering
- Vibrations
- Monitoring
- Predictive models
- Data models
- Training
- Data acquisition
- Global dependencies
- health perception
- industrial Internet of Things (IIoT)
- instant intelligent predictive maintenance (IPdM)
- self-attention
- sequence prediction
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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