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

Congfang Huang (), David Blondheim () and Shiyu Zhou ()
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Congfang Huang: University of Wisconsin-Madison
David Blondheim: Mercury Marine, a Division of Brunswick Corporation
Shiyu Zhou: University of Wisconsin-Madison

Journal of Intelligent Manufacturing, 2025, vol. 36, issue 6, No 35, 4389-4409

Abstract: 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.

Keywords: Anomaly detection; X-ray image; Convolutional variational autoencoder; Generative adversarial networks; Tensor decomposition; Pattern extraction (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10845-024-02435-x

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