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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Veröffentlicht in:Journal of intelligent manufacturing Jg. 36; H. 6; S. 4389 - 4409
Hauptverfasser: Huang, Congfang, Blondheim, David, Zhou, Shiyu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.08.2025
Springer Nature B.V
Schlagworte:
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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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:0956-5515
1572-8145
DOI:10.1007/s10845-024-02435-x