Explainable knowledge graph embeddings for industrial process monitoring & control

Abstract

AI-driven solutions are being employed in process monitoring and control to learn typical system behaviours under various conditions based on historical data. However, they are unable to take advantage of the rich, tacit domain expertise of experienced process engineers pertaining to these behaviours. Hybrid AI solutions are designed to fuse domain knowledge into machine learning models, but have so far been limited to specific industrial subdomains or applications and support only domain expertise in the form of equations. We propose an explainable hybrid AI methodology that can integrate any kind of tacit knowledge in an interpretable manner. First, we introduce a method to consolidate process data and domain-specific expertise in a generic fashion using Knowledge Graphs. Second, we propose a Knowledge Graph transformation technique to better capture the sequential aspects of a process and an accompanying white-box Knowledge Graph embedding technique that allows us to integrate domain knowledge directly into the feature space of a data-driven model. Third, we show how our methodology can be combined with explainability techniques, such as SHAP, to highlight directly in the graph which paths contributed most to the AI-driven decision. Our methodology has been evaluated on two real-world chemical engineering use cases. It outperforms data-driven baselines on all performance metrics, with average improvements of up to 8.57% and 10.21%.