Reliability-improved machine learning model using knowledge-embedded learning approach for smart manufacturing

Abstract

Machine learning models play a crucial role in smart manufacturing by revolutionizing industrial automation so as to boost productivity and product quality. However, the reliability of these models often faces challenges from factors such as data drift, concept drift, adversarial attacks, and increasing model complexity. In addressing these challenges, this paper proposes a novel approach called Reliability Improved Machine Learning (RIML), which leverages on prior knowledge by incorporating it into the machine learning pipeline through a secondary output that is easily verifiable and assessable within the application domain. Built upon the Knowledge-embedded Machine Learning (KML) framework, RIML differs from conventional strategies by modifying the model’s architecture. In its implementation, additional layers were introduced, specifically designed to identify and discard misclassified cases to improve the model’s reliability. RIML’s efficacy was successfully demonstrated through a simulated dataset and three real use-case studies, namely, a general walk/run scenario, an industry-related case using metro railway dataset, and a smart manufacturing application on gas detection. The promising results highlighted RIML’s ability to significantly reduce misclassifications, thereby enhancing model reliability in diverse real-world scenarios.