A Logic-Guided Intelligent Multidefect Detection System for Microarmatures

Defect detection for non-standard industrial parts like micro induction motors has been important issue in industrial control systems. The constructional imperfections of armatures hold a large proportion of the reported induction motor failures, for which the detection methods are mostly manual inspection or image classification neural networks. However, the end-to-end networks mentioned above cannot accurately distinguish the defect types, locations, and specific state simultaneously. In this paper, the Logic-guided Intelligent Defect Detection System (LIDDS) has been proposed to address the issue above. The system consists of the Modified Feature Extraction (MFE) module, Logic-guided Classification (LC) unit, and Defect Extent Estimation (DEE) unit. The MFE module deployed attention mechanisms to enhance the ability of learning multi-scale features over background noise. The LC unit is achieved under cross-referencing multiple conditions, to jointly detect the defects with strict size constraints. Furthermore, the DEE unit estimates the extent of the complicated defects to facilitate future severity research. For evaluation, the microarmature surface feature dataset containing defects and auxiliary features is established. Experimental results indicate that the proposed system has an overall high F1 score and accuracy for the armature defect detection and classification. Meanwhile, it shows robust precision under motion and optical perturbations, supporting severity assessment in industrial environments. The dataset of this article is open source and available https://github.com/ashEsto475/ microarmature_surface_feature_dataset © 2024 IEEE.