XAI for Predictive Maintenance

The field of Explainable Predictive Maintenance (PM) is concerned with developing methods that can clarify how AI systems operate in the PM domain. One of the challenges of creating maintenance plans is integrating AI output with human decision-making processes and expertise. For AI to be helpful and trustworthy, fault predictions must be contextualized and easily comprehensible to humans. This involves providing tailored explanations to different actors depending on their roles and needs. For example, engineers can be connected to technical installation blueprints, while managers can evaluate system downtime costs, and lawyers can assess safety-threatening failures' potential liability. In many industries, black-box AI systems analyze sensor data to predict failures by detecting anomalies and deviations from typical behavior with impressive accuracy. However, PM is just one part of a broader context that aims to identify the most probable causes, develop a recovery plan, and estimate remaining useful life while providing alternative solutions. Achieving this requires complex interactions among various actors in industrial and decision-making processes. Our tutorial explores current trends, and promising research directions in Explainable AI (XAI) relevant to Explainable Predictive Maintenance (XPM), and future challenges and open issues on this topic. We will also present three case studies that highlight XPM's challenges in bus and train operations and steel factories. © 2023 Owner/Author.
Funding: João Gama was financed by European Union’s Horizon Europe research and innovation programme under the Grant Agreement 101073874 (EMERITUS). Sławomir Nowaczyk and Sepideh Pashami were financed by Swedish Research Council under grant CHIST- ERA-19-XAI-012. Rita P. Ribeiro was financed by the CHIST-ERA grant CHIST-ERA- 19-XAI-012, and project CHIST-ERA/0004/2019 funded by FCT and also within project LA/P/0063/2020.