Key Elements to Contextualize AI-Driven Condition Monitoring Systems towards Their Risk-Based Evaluation

Abstract

Industrial users can be justifiably hesitant in adopting Condition Monitoring Systems (CMSs) unless evidence indicates benefits from their use. Measuring a CMS’s ability to prevent losses is difficult and lacks standard procedures. The increasing availability of closed-box Artificial Intelligence (AI)- driven CMSs exacerbates the hesitancy as predicting their impacts is more challenging. This paper details three key elements critical to evaluating CMS impact:(1) the Application Area, (2) the Risk Management Processes, and (3) the Monitoring Mechanism. This paper discusses these elements in their capacity to contextualize a CMS’s role within an asset’s risk management processes, which can lead to justifying CMS use.