LAPSE:2020.0159
Published Article
LAPSE:2020.0159
Identification of Abnormal Processes with Spatial-Temporal Data Using Convolutional Neural Networks
Yumin Liu, Zheyun Zhao, Shuai Zhang, Uk Jung
February 3, 2020
Identifying abnormal process operation with spatial-temporal data remains an important and challenging work in many practical situations. Although spatial-temporal data identification has been extensively studied in some domains, such as public health, geological condition, and environment pollution, the challenge associated with designing accurate and convenient recognition schemes is very rarely addressed in modern manufacturing processes. This paper proposes a general recognition framework for identifying abnormal process with spatial-temporal data by employing a convolutional neural network (CNN) model. Firstly, motivated by the pasting case study, the spatial-temporal data are transformed into process images for capturing spatial and temporal interrelationship. Then, the CNN recognition model is presented for identifying different types of these process images, leading to the identification of abnormal process with spatial-temporal data. The specific architecture parameters of CNN are determined step by step. According to the performance comparison with alternative methods, the proposed method is able to accurately identify the abnormal process with spatial-temporal data.
Keywords
convolutional neural network, pasting process, process image, spatial-temporal data
Suggested Citation
Liu Y, Zhao Z, Zhang S, Jung U. Identification of Abnormal Processes with Spatial-Temporal Data Using Convolutional Neural Networks. (2020). LAPSE:2020.0159
Author Affiliations
Liu Y: Business School, Zhengzhou University, Zhengzhou 450001, China
Zhao Z: Business School, Zhengzhou University, Zhengzhou 450001, China [ORCID]
Zhang S: Business School, Zhengzhou University, Zhengzhou 450001, China
Jung U: Department of Management, School of Business, Dongguk University-Seoul, Seoul 04620, Korea [ORCID]
Journal Name
Processes
Volume
8
Issue
1
Article Number
E73
Year
2020
Publication Date
2020-01-06
Published Version
ISSN
2227-9717
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Original Submission
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PII: pr8010073, Publication Type: Journal Article
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LAPSE:2020.0159
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doi:10.3390/pr8010073
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Feb 3, 2020
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Calvin Tsay
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