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A comparison study on anomaly detection methods in manufacturing process monitoring with X-ray images

Author

Listed:
  • Congfang Huang

    (University of Wisconsin-Madison)

  • David Blondheim

    (Mercury Marine, a Division of Brunswick Corporation)

  • Shiyu Zhou

    (University of Wisconsin-Madison)

Abstract

Process monitoring and anomaly detection in manufacturing production systems are of vital importance in the consistency, reliability, and quality of manufacturing operations, which motivates the development of a large number of anomaly detection methods. In this work, a comparison study on representative unsupervised X-ray image based anomaly detection methods is conducted. Statistical, physical, and deep learning based dimension reduction methods and different anomaly detection criteria are considered and compared. Case studies on real-world X-ray image data with simulated anomalies and real anomalies are both carried out. Grey level co-occurrence matrix with Hotelling $$T^2$$ T 2 statistic as detection criteria achieves the best performance on the simulated anomalies case with an overall detection accuracy of 96%. Principal component analysis with reconstruction error as criteria obtains the highest detection rate of 90.6% in the real anomalies case. Considering the ever-growing availability of image data in intelligent manufacturing processes, this study will provide very useful knowledge and find a broad audience.

Suggested Citation

  • Congfang Huang & David Blondheim & Shiyu Zhou, 2025. "A comparison study on anomaly detection methods in manufacturing process monitoring with X-ray images," Journal of Intelligent Manufacturing, Springer, vol. 36(6), pages 4389-4409, August.
    • Handle: RePEc:spr:joinma:v:36:y:2025:i:6:d:10.1007_s10845-024-02435-x
    DOI: 10.1007/s10845-024-02435-x
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    References listed on IDEAS

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    1. 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.
    2. Wenqing Wang & Junpeng Bao & Tao Li, 2021. "Correction to: Bound smoothing based time series anomaly detection using multiple similarity measures," Journal of Intelligent Manufacturing, Springer, vol. 32(6), pages 1791-1791, August.
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    6. Wenqing Wang & Junpeng Bao & Tao Li, 2021. "Bound smoothing based time series anomaly detection using multiple similarity measures," Journal of Intelligent Manufacturing, Springer, vol. 32(6), pages 1711-1727, August.
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