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Open AccessArticle

Development of Scalable On-Line Anomaly Detection System for Autonomous and Adaptive Manufacturing Processes

1
Department of Industrial and Systems Engineering, Dongguk University, 3ga, Pil-dong, Jung-gu, Seoul 04620, Korea
2
Department of Systems Management Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon, Gyeonggi-do 16419, Korea
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(21), 4502; https://doi.org/10.3390/app9214502
Received: 24 September 2019 / Revised: 14 October 2019 / Accepted: 18 October 2019 / Published: 24 October 2019
(This article belongs to the Section Computing and Artificial Intelligence)
Factories of the future are foreseen to evolve into smart factories with autonomous and adaptive manufacturing processes. However, the increasing complexity of the network of manufacturing processes is expected to complicate the rapid detection of process anomalies in real time. This paper proposes an architecture framework and method for the implementation of the Scalable On-line Anomaly Detection System (SOADS), which can detect process anomalies via real-time processing and analyze large amounts of process execution data in the context of autonomous and adaptive manufacturing processes. The design of this system architecture framework entailed the derivation of standard subsequence patterns using the PrefixSpan algorithm, a sequential pattern algorithm. The anomalies of the real-time event streams and derived subsequence patterns were scored using the Smith-Waterman algorithm, a sequence alignment algorithm. The excellence of the proposed system was verified by measuring the time for deriving subsequence patterns and by obtaining the anomaly scoring time from large event logs. The proposed system succeeded in large-scale data processing and analysis, one of the requirements for a smart factory, by using Apache Spark streaming and Apache Hbase, and is expected to become the basis of anomaly detection systems of smart factories. View Full-Text
Keywords: autonomous and adaptive manufacturing process; smart factory; Big Data; NoSQL; subsequence pattern autonomous and adaptive manufacturing process; smart factory; Big Data; NoSQL; subsequence pattern
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MDPI and ACS Style

Choi, S.; Youm, S.; Kang, Y.-S. Development of Scalable On-Line Anomaly Detection System for Autonomous and Adaptive Manufacturing Processes. Appl. Sci. 2019, 9, 4502. https://doi.org/10.3390/app9214502

AMA Style

Choi S, Youm S, Kang Y-S. Development of Scalable On-Line Anomaly Detection System for Autonomous and Adaptive Manufacturing Processes. Applied Sciences. 2019; 9(21):4502. https://doi.org/10.3390/app9214502

Chicago/Turabian Style

Choi, Seunghyun; Youm, Sekyoung; Kang, Yong-Shin. 2019. "Development of Scalable On-Line Anomaly Detection System for Autonomous and Adaptive Manufacturing Processes" Appl. Sci. 9, no. 21: 4502. https://doi.org/10.3390/app9214502

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