Industrial Anomaly Detection System: A Multicase Algorithm Leveraging Feature Information and Memory Bank

• Published in: IEEE transactions on instrumentation and measurement Vol. 74; pp. 1 - 9
• Main Authors: Shi, Yunhan, Gao, Bin, Yang, Geng, Li, Haoran, Lok Woo, Wai
• Format: Journal Article
• Language: English
• Published: New York IEEE 2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Ablation; Algorithms; Anomalies; Anomaly detection; Anomaly detection (AD); Clustering algorithms; defect localization and segmentation; Extraterrestrial measurements; Feature extraction; Input variables; Learning; Measurement; metric learning; Modules; multicase generalization; nondestructive testing; Overfitting; Testing; Training; Vectors
• DOI: 10.1109/TIM.2025.3551852
• ISSN: 0018-9456

The conventional anomaly detection (AD) methods typically rely on training normal samples without defects to identify deviations from the background. However, these methods suffer from issues, such as missing detection or false detection. Although subsequent research has attempted to improve algorithm performance, this often results in overfitting to specific cases within the AD framework. To address these challenges, this article proposes a semi-supervised AD algorithm that combines improved metric learning techniques with a memory bank (MB) update module. In order to enhance the algorithm's generalization capabilities across different cases, a multicases MB inheritance approach is introduced. This approach facilitates rapid generalization to unknown test cases with minimal iterative learning (<inline-formula> <tex-math notation="LaTeX">\le 5 </tex-math></inline-formula> epochs). Additionally, a bank-case matching module is designed to select the appropriate MB and calculate anomaly scores within our framework. The effectiveness of the proposed algorithm has been validated through real industrial tests and ablation experiments, demonstrating its capability in detecting anomalies accurately and reliably. Code is available on: https://github.com/FrankCloud-UESTC/Multi-case-AD .