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World Conference on Information Systems and Technologies

WorldCIST 2022: Information Systems and Technologies pp 22–31Cite as

A Novel Clustering Anomaly Detection of PCA Based Time Series Features with CNC Machines Data

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Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 468))

Abstract

With the 4th industrial revolution, the manufacturing technology of computer numerical control (CNC) has been one of the irreplaceable important technologies in the manufacturing industry. If the factory’s machinery stops working for some reason, such as overheating or wear and tear, the damage to the factory is very high. Therefore, in order to prevent this in advance, research on a method for detecting anomalies using data collected from a machine is currently being actively conducted. This paper presents a method for detecting anomalies using only the data received from the machine without using additionally installed sensors and visually expressing them. Anomaly detection was successfully performed on both the actually collected dataset and the open dataset for validation using the clustering method after principal component analysis using the tendency.

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Acknowledgements

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. 2021R1F1A1060054) and supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2021–2018-0–01417) supervised by the IITP(Institute for Information & Communications Technology Planning & Evaluation) and supported by the MSIT(Ministry of Science and ICT), Korea, under the ICT Creative Consilience Program(IITP-2021–2020-0–01821) supervised by the IITP(Institute for Information & communications Technology Planning & Evaluation, Corresponding author: Prof. Jongpil Jeong.

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Authors and Affiliations

  1. Sungkyunkwan University, 2066, Seobu-ro, Jangan-gu, Suwon-si, Gyeonggi-do, Republic of Korea

    Hyeonjong Ha, Dongwhan Min & Jongpil Jeong

Authors
  1. Hyeonjong Ha
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  2. Dongwhan Min
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  3. Jongpil Jeong
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Corresponding author

Correspondence to Jongpil Jeong .

Editor information

Editors and Affiliations

  1. ISEG, Universidade de Lisboa, Lisbon, Portugal

    Alvaro Rocha

  2. College of Engineering, The Ohio State University, Columbus, OH, USA

    Hojjat Adeli

  3. Institute of Data Science and Digital Technologies, Vilnius University, Vilnius, Lithuania

    Gintautas Dzemyda

  4. DCT, Universidade Portucalense, Porto, Portugal

    Fernando Moreira

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Ha, H., Min, D., Jeong, J. (2022). A Novel Clustering Anomaly Detection of PCA Based Time Series Features with CNC Machines Data. In: Rocha, A., Adeli, H., Dzemyda, G., Moreira, F. (eds) Information Systems and Technologies. WorldCIST 2022. Lecture Notes in Networks and Systems, vol 468. Springer, Cham. https://doi.org/10.1007/978-3-031-04826-5_3

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