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Open AccessArticle

Identification of Abnormal Processes with Spatial-Temporal Data Using Convolutional Neural Networks

1
Business School, Zhengzhou University, Zhengzhou 450001, China
2
Department of Management, School of Business, Dongguk University-Seoul, Seoul 04620, Korea
*
Author to whom correspondence should be addressed.
Processes 2020, 8(1), 73; https://doi.org/10.3390/pr8010073
Received: 2 December 2019 / Revised: 25 December 2019 / Accepted: 2 January 2020 / Published: 6 January 2020
(This article belongs to the Special Issue Advanced Process Monitoring for Industry 4.0)
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. View Full-Text
Keywords: spatial-temporal data; pasting process; process image; convolutional neural network spatial-temporal data; pasting process; process image; convolutional neural network
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MDPI and ACS Style

Liu, Y.; Zhao, Z.; Zhang, S.; Jung, U. Identification of Abnormal Processes with Spatial-Temporal Data Using Convolutional Neural Networks. Processes 2020, 8, 73.

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