Method for Detecting Industrial Defects in Intelligent Manufacturing Using Deep Learning

With the advent of Industry 4.0, marked by a surge in intelligent manufacturing, advanced sensors embedded in smart factories now enable extensive data collection on equipment operation. The analysis of such data is pivotal for ensuring production safety, a critical factor in monitoring the health status of manufacturing apparatus. Conventional defect detection techniques, typically limited to specific scenarios, often require manual feature extraction, leading to inefficiencies and limited versatility in the overall process. Our research presents an intelligent defect detection methodology that leverages deep learning techniques to automate feature extraction and defect localization processes. Our proposed approach encompasses a suite of components: the high-level feature learning block (HLFLB), the multi-scale feature learning block (MSFLB), and a dynamic adaptive fusion block (DAFB), working in tandem to extract meticulously and synergistically aggregate defect-related characteristics across various scales and hierarchical levels. We have conducted validation of the proposed method using datasets derived from gearbox and bearing assessments. The empirical outcomes underscore the superior defect detection capability of our approach. It demonstrates consistently high performance across diverse datasets and possesses the accuracy required to categorize defects, taking into account their specific locations and the extent of damage, proving the method’s effectiveness and reliability in identifying defects in industrial components.