Cost-Efficiency and Cost-Effectiveness of XAI in Predictive Maintenance

Abstract

Predictive maintenance has become a fundamental approach in industrial environments to monitor the condition of equipment in order to prevent unexpected failures in machines through the data analysis of the sensors. Conventional artificial intelligence models can predict potential failures based on parameters such as temperature, vibration and pressure. However, these models are often black boxes and offer little interpretability in terms of why they are making the predictions. The lack of explanation can result in decreased trust among the maintenance personnel, as a consequence, unnecessary repairs, longer diagnostic time, and inefficient resources allocation. Explainable Artificial Intelligence overcomes this limitation and gives the ability for understanding the contributing factors of the predicted failures and this leads to more informed and targeted maintenance actions. This research investigates the economic aspects that integrating explainability into prediction maintenance systems poses, both as cost-efficiency and cost-effectiveness. Cost-efficiency is measured in terms of the reduction of unnecessary maintenance interventions and downtime, and cost-effectiveness is measured in terms of the long-term financial benefits in comparison to initial implementation efforts. By facilitating clarity of decisions and improving the transparency of the information, XAI has the potential to improve operational reliability and optimize maintenance strategies. The analysis shows how explainable models can play a role in the sustainable operation of industry by balancing technical performance against economic goals.