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Temporal anomaly detection on IIoT-enabled manufacturing

Author

Listed:
  • Peng Zhan

    (Shandong University)

  • Shaokun Wang

    (Shandong University)

  • Jun Wang

    (Shandong University)

  • Leigang Qu

    (Shandong University)

  • Kun Wang

    (Shandong University)

  • Yupeng Hu

    (Shandong University)

  • Xueqing Li

    (Shandong University)

Abstract

Along with the coming of industry 4.0 era, industrial internet of things (IIoT) plays a vital role in advanced manufacturing. It can not only connect all equipment and applications in manufacturing processes closely, but also provide oceans of sensor data for real-time work-in-process monitoring. Considering the corresponding abnormalities existing in these sensor data sequences, how to effectively implement temporal anomaly detection is of great significance for smart manufacturing. Therefore, in this paper, we proposed a novel time series anomaly detection method, which can effectively recognize corresponding abnormalities within the given time series sequences by standing on the hierarchical temporal representation. Extensive comparison experiments on the benchmark datasets have been conducted to demonstrate the superiority of our method in term of detection accuracy and efficiency on IIOT-enabled manufacturing.

Suggested Citation

  • Peng Zhan & Shaokun Wang & Jun Wang & Leigang Qu & Kun Wang & Yupeng Hu & Xueqing Li, 2021. "Temporal anomaly detection on IIoT-enabled manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 32(6), pages 1669-1678, August.
    • Handle: RePEc:spr:joinma:v:32:y:2021:i:6:d:10.1007_s10845-021-01768-1
    DOI: 10.1007/s10845-021-01768-1
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    References listed on IDEAS

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    1. Chayma Sellami & Carlos Miranda & Ahmed Samet & Mohamed Anis Bach Tobji & François de Beuvron, 2020. "On mining frequent chronicles for machine failure prediction," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 1019-1035, April.
    2. Ying Cheng & Luning Bi & Fei Tao & Ping Ji, 2020. "Hypernetwork-based manufacturing service scheduling for distributed and collaborative manufacturing operations towards smart manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1707-1720, October.
    3. Hui Yang & Soundar Kumara & Satish T.S. Bukkapatnam & Fugee Tsung, 2019. "The internet of things for smart manufacturing: A review," IISE Transactions, Taylor & Francis Journals, vol. 51(11), pages 1190-1216, November.
    4. Chia-Yu Hsu & Wei-Chen Liu, 2021. "Multiple time-series convolutional neural network for fault detection and diagnosis and empirical study in semiconductor manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 823-836, March.
    5. Lei Dong & Peng Wang & Fang Yan, 2019. "Damage forecasting based on multi-factor fuzzy time series and cloud model," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 521-538, February.
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    Cited by:

    1. Akshansh Mishra & Anish Dasgupta, 2022. "Supervised and Unsupervised Machine Learning Algorithms for Forecasting the Fracture Location in Dissimilar Friction-Stir-Welded Joints," Forecasting, MDPI, vol. 4(4), pages 1-11, September.

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