DuAK: Reinforcement Learning-Based Knowledge Graph Reasoning for Steel Surface Defect Detection

Surface defect is a crucial factor affecting the product quality of steel products. Current studies mainly focus on defect recognition and classification using machine vision-based algorithms, which lack the trace of potential causes and the reuse of experiential knowledge. To address this issue, we construct a knowledge graph for steel surface defects by fusing the multi-source and heterogeneous industrial data, including process parameters, chemical compositions, defect images, operation logs and empirical knowledge. A policy-based reinforcement learning approach is developed to solve the path reasoning problem over the industrial knowledge graph in defect detection and diagnosis. The approach employs two agents to explore the path efficiently from opposite directions, utilizes an integrated reward function that comprehensively considers the path direction, path length and entity distance to perform action selection, and adopts the path sharing mechanism and the prior knowledge to update selection policy. Experimental comparisons with the state-of-the-art knowledge reasoning algorithms on two benchmark datasets, NELL-995 and FB15K-237, validate the performance and merits of the proposed method. The effectiveness of the proposed method is also evaluated on a practical steel surface defect dataset, and the results show that our approach performs well in knowledge reasoning on the surface defect graph. Note to Practitioners—The surface quality of products has become a widely concerned focus in manufacturing industries. With the development of industrial IoT and Cyber-physical system technologies, more and more industrial data has been collected, and machine learning-based algorithms have been developed and applied to the recognition of detect defects. However, the algorithms do not take full advantage of the multi-source and heterogeneous defect-related data. On the other hand, it is also difficult to accumulate, inherit and reuse the experts’ knowledge of solving historical cases in the long-term production process. In order to deal with the above obstacles, we apply the knowledge graph for steel surface defect detection. In the proposed approach, a policy-based reinforcement learning algorithm is developed to solve the path reasoning problem over the industrial knowledge graph. To further improve the performance of our algorithm, we employ two agents to explore the path efficiently from opposite directions, utilize an integrated reward function which comprehensively considers the path direction, path length and entity distance to perform action selection, adopt the path sharing mechanism and updated selection policy to reuse the prior knowledge. As a result, our algorithm obtains high precision in knowledge reasoning tasks on two benchmark datasets and a practical steel surface defect dataset compared with some existing algorithms. Hence, it can be readily applied to real surface defect detection problems and facilitates intelligent manufacturing in steel production.