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Spatial randomness-based anomaly detection approach for monitoring local variations in multimode surface topography

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Listed:
  • Jaeseung Baek

    (Northern Michigan University)

  • Myong K. Jeong

    (Rutgers University)

  • Elsayed A. Elsayed

    (Rutgers University)

Abstract

Anomaly detection of three-dimensional (3D) topographic data is a challenging problem in spatial data analysis. In this paper, we investigate spatial patterns of 3D surface data that exhibit multiple in-control modes. In complex manufacturing processes, surfaces of final products could contain different topographic features from one in-control surface to another, thus making it difficult to monitor the surface with existing approaches, which rely on the assumption of the presence of single mode surface topography. We propose a novel anomaly detection approach for monitoring local topographic variations in the presence of multimode surface topography. We present a binarization model to capture the generic behavior of the multimode surfaces and enhance the representation of the surface. To systematically monitor the surface, we introduce a new probabilistic distance measure (PDM) that quantifies the similarity of spatial patterns between two binarized surfaces. The proposed PDM takes advantage of identifying local variations by utilizing the order neighbor statistics, which captures the local property on the surface. Experimental results with numerical simulation data and real-life paper surface data are provided to demonstrate the effectiveness of the proposed approach.

Suggested Citation

  • Jaeseung Baek & Myong K. Jeong & Elsayed A. Elsayed, 2024. "Spatial randomness-based anomaly detection approach for monitoring local variations in multimode surface topography," Annals of Operations Research, Springer, vol. 341(1), pages 173-195, October.
    • Handle: RePEc:spr:annopr:v:341:y:2024:i:1:d:10.1007_s10479-023-05468-2
    DOI: 10.1007/s10479-023-05468-2
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    References listed on IDEAS

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    1. Linmiao Zhang & Kaibo Wang & Nan Chen, 2016. "Monitoring wafers’ geometric quality using an additive Gaussian process model," IISE Transactions, Taylor & Francis Journals, vol. 48(1), pages 1-15, January.
    2. Prahalad K. Rao & Omer F. Beyca & Zhenyu (James) Kong & Satish T. S. Bukkapatnam & Kenneth E. Case & Ranga Komanduri, 2015. "A graph-theoretic approach for quantification of surface morphology variation and its application to chemical mechanical planarization process," IISE Transactions, Taylor & Francis Journals, vol. 47(10), pages 1088-1111, October.
    3. Lian Duan & Lida Xu & Ying Liu & Jun Lee, 2009. "Cluster-based outlier detection," Annals of Operations Research, Springer, vol. 168(1), pages 151-168, April.
    4. Ali Tosyali & Jinho Kim & Jeongsub Choi & Yunyi Kang & Myong K. Jeong, 2020. "New node anomaly detection algorithm based on nonnegative matrix factorization for directed citation networks," Annals of Operations Research, Springer, vol. 288(1), pages 457-474, May.
    5. Yuxin Zhao & Shuai Chang & Chang Liu, 2015. "Multifractal theory with its applications in data management," Annals of Operations Research, Springer, vol. 234(1), pages 133-150, November.
    6. Marco Grasso & Bianca Maria Colosimo & Fugee Tsung, 2017. "A phase I multi-modelling approach for profile monitoring of signal data," International Journal of Production Research, Taylor & Francis Journals, vol. 55(15), pages 4354-4377, August.
    7. Jaeseung Baek & Myong K. Jeong & Elsayed A. Elsayed, 2023. "Monitoring variations in multimode surface topography," International Journal of Production Research, Taylor & Francis Journals, vol. 61(4), pages 1129-1145, February.
    8. Shannon M. Fast & Louis Kim & Emily L. Cohn & Sumiko R. Mekaru & John S. Brownstein & Natasha Markuzon, 2018. "Predicting social response to infectious disease outbreaks from internet-based news streams," Annals of Operations Research, Springer, vol. 263(1), pages 551-564, April.
    9. Anh Tuan Bui & Daniel W. Apley, 2022. "Robust monitoring of stochastic textured surfaces," International Journal of Production Research, Taylor & Francis Journals, vol. 60(16), pages 5071-5086, August.
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