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

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 represent...

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Vydané v:	Journal of intelligent manufacturing Ročník 36; číslo 6; s. 4389 - 4409
Hlavní autori:	Huang, Congfang, Blondheim, David, Zhou, Shiyu
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.08.2025 Springer Nature B.V
Predmet:	Anomalies Business and Management Control Criteria Decomposition Deep learning Hypothesis testing Intelligent manufacturing systems Machine learning Machines Manufacturing Manufacturing industry Mechatronics Monitoring Principal components analysis Processes Production Robotics Statistical analysis Statistical methods Statistics Tensor decomposition X-ray image Generative adversarial networks Anomaly detection Convolutional variational autoencoder Pattern extraction
ISSN:	0956-5515, 1572-8145
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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 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.
AbstractList	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 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. 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 T2 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.
Author	Blondheim, David Zhou, Shiyu Huang, Congfang
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Snippet	Process monitoring and anomaly detection in manufacturing production systems are of vital importance in the consistency, reliability, and quality of...
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SubjectTerms	Anomalies Business and Management Control Criteria Decomposition Deep learning Hypothesis testing Intelligent manufacturing systems Machine learning Machines Manufacturing Manufacturing industry Mechatronics Monitoring Principal components analysis Processes Production Robotics Statistical analysis Statistical methods Statistics
Title	A comparison study on anomaly detection methods in manufacturing process monitoring with X-ray images
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