Semi-Supervised Condition Monitoring and Visualization of Fused Magnesium Furnace

Autor:	Lu Shaowen, Yixin Wen
Rok vydání:	2022
Předmět:	business.industry Computer science Real-time computing Condition monitoring Cloud computing Edge server Visualization Task (computing) Control and Systems Engineering Feature (computer vision) Classifier (linguistics) Electrical and Electronic Engineering Distributed structure business
Zdroj:	IEEE Transactions on Automation Science and Engineering. 19:3471-3482
ISSN:	1558-3783 1545-5955
DOI:	10.1109/tase.2021.3124015
Popis:	This paper introduces a novel practice of using image based condition classification and visualization system to augment operators in the task of monitoring the working condition of fused magnesium furnace. The system implements two functions: working condition detection and remote visually reconstruction of the furnace flame. For the problem of working condition detection, we combine the image features and the smelting electrical currents to train the classifier under semi-supervised learning framework. We also introduce a highly efficient cross-entropy based optimization method for training. For the visualization task, we propose a practical end-to-end solution which can visually simulate the dynamic furnace flame at remote monitoring consoles according to the visual feature of the monitoring video. Finally, we introduce the distributed structure of the monitoring system which consists of a private cloud, an edge server and remote monitoring consoles. The proposed solution can be applicable for various monitoring tasks in industry.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::19ef197a3a004d0892f8e3ba3c3849a0 https://doi.org/10.1109/tase.2021.3124015 Zobrazit plný text záznamu