processes Article Identification of Abnormal Processes with Spatial-Temporal Data Using Convolutional Neural Networks Yumin Liu 1 , Zheyun Zhao 1, * , Shuai Zhang 1 and Uk Jung 2 1 Business School, Zhengzhou University, Zhengzhou 450001, China; yuminliu@zzu.edu.cn (Y.L.); zhang1227zzu@163.com (S.Z.) 2 Department of Management, School of Business, Dongguk University-Seoul, Seoul 04620, Korea; ukjung@dongguk.edu * Correspondence: zzu_zzy@126.com Received: 2 December 2019; Accepted: 2 January 2020; Published: 6 January 2020   Abstract: 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: spatial-temporal data; pasting process; process image; convolutional neural network 1. Introduction Advanced sensing technologies are being increasingly applied in data collection systems for the areas including public health, geological condition, environment pollution, and manufacturing process. If the output of sensors is represented by the data with space and time structure, it can be termed as spatial-temporal data [1]. A lot of research focuses on the abnormality identification of abnormal-spatial temporal data, such as identifying outliers of the hourly air quality [2], detecting abnormal ozone measurements caused by air pollution or correlation among neighbor sensors [3], and diagnosing whether a disease is randomly distributed over space and time [4]. With the development of manufacturing technology, many sensors have been installed in the production lines, and a large number of spatial-temporal data can be collected from such processes. In order to improve the quality of manufacturing process, the abnormality identification of such spatial-temporal data has attracted much attention. Wang et al. [5] proposed a spatial-temporal data modeling method to identify the abnormality of a wafer production process. The identification scheme developed by Megahed et al. [6] can quickly detect the emergence of a fault in the nonwoven textile production process. Yu et al. [1] presented a rapid spatial-temporal quality control procedure for detecting systematic and random outliers. Current research is being conducted on identifying whether the process with spatial-temporal data is normal or not. Their common objective is to accurately detect the time and location of changes in Processes 2020, 8, 73; doi:10.3390/pr8010073 www.mdpi.com/journal/processes