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The Framework and Applications of Anomalous Subsequence Detection in Streaming Data Analysis and Process Monitoring in Intelligent Manufacturing

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Systems Collaboration and Integration (ICPR1 2021)

Part of the book series: Automation, Collaboration, & E-Services ((ACES,volume 14))

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Abstract

In intelligent manufacturing, detecting anomalous signals or time series subsequences and monitoring the process shift are two fundamental tasks to maintain manufacturing quality. Detecting the anomalous signals or process shifting as early as possible is a challenge. This chapter reviews the relevant research works in these two domains and presents the state-of-art methodologies that combine machine learning and data mining techniques to resolve the practical problem in production, named local recurrence rate with robust k-means (LRR-RKMeans) and long short-term memory real-time contrasts control chart (LSTM-RTC). The real-world data in manufacturing were used to evaluate the methods. The methods for anomalous subsequence detection on process monitoring and real-time contrasts control chart for quality control were applied to real-world semiconductor wafer and white wine production cases. The experimental results show that LRR-RKMeans can detect the defective wafer accurately, and LSTM-RTC has a low response delay in noticing process shift in white wine production. The possibility of combining the proposed data analysis framework with an edge-cloud computing system to alleviate the performance boundary in the computational time was further discussed.

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Author information

Authors and Affiliations

  1. Institute of Statistical Science, Academia Sinica, No. 128, Academia Road, Section 2, Nankang, Taipei, 11529, Taiwan

    Hendri Sutrisno

  2. Department of Industrial Management, National Taiwan University of Science and Technology, No. 43, Section 4, Keelung Rd, Da’an District, Taipei City, 106, Taiwan

    Chao-Lung Yang

Authors
  1. Hendri Sutrisno
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  2. Chao-Lung Yang
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Corresponding author

Correspondence to Hendri Sutrisno .

Editor information

Editors and Affiliations

  1. Industrial Engineering and Enterprise Information, Tunghai University, Taichung, Taiwan

    Chin-Yin Huang

  2. Department of Systems Science and Industrial Engineering, State University of New York at Binghamton, New York, NY, USA

    Sang Won Yoon

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Sutrisno, H., Yang, CL. (2023). The Framework and Applications of Anomalous Subsequence Detection in Streaming Data Analysis and Process Monitoring in Intelligent Manufacturing. In: Huang, CY., Yoon, S.W. (eds) Systems Collaboration and Integration. ICPR1 2021. Automation, Collaboration, & E-Services, vol 14. Springer, Cham. https://doi.org/10.1007/978-3-031-44373-2_14

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