Deep understanding in industrial processes by complementing human expertise with interpretable patterns of machine learning

Abstract Experts in industrial processes rely on domain knowledge (DK) repositories to identify the causes of abnormal situations in order to make appropriate decisions that mitigate the negative effects of such events. These DK repositories need to be enriched and updated continuously as different unexpected events occur. A common causality analysis method in DK repositories is the fault tree analysis (FTA). The major limitation of updating a fault tree is that it requires in-depth system knowledge, which involves a high level of human experience. Data exploitation based on machine learning (ML) can address this limitation by deeply analyzing process historical data to discover hidden phenomena that are difficult for human experts to identify and to analyze. This paper proposes an innovative methodology that combines domain knowledge, in the form of FTA, with additional knowledge extracted by a descriptive ML method called logical analysis of data (LAD). More specifically, LAD is a classification method, which provides as a by-product a set of interpretable rules (patterns) explaining the classification results. The patterns extracted from historical data represent an important and complementary source of knowledge that provides experts with insights and allows them to better understand the process operations. The objective of using these patterns in the proposed methodology is to provide automatic enrichment and updating of existing fault trees in order to achieve accurate fault detection and diagnosis (FDD) in industrial processes. The proposed methodology is demonstrated using fault trees constructed for two different systems in the process industry. The fault tree for each system was updated successfully with minimal effort from process experts.