A multi-dimensional framework and novel indices for sustainable assessment of intelligent fault detection and diagnosis in smart manufacturing

In the direction of Smart Manufacturing (SM) and sustainable production, a crucial challenge lies in synchronizing operational excellence with sustainable practices. Intelligent Fault Detection and Diagnosis (I-FDD) is crucial for minimizing downtime and maintenance costs, as well as extending machinery life cycles. However, standard assessment methods prioritize operational efficiency over sustainability impacts. This article proposes a multi-dimensional sustainability assessment framework for I-FDD, explicitly integrating technical and sustainability considerations. Moreover, three indices are introduced to balance between technical performance and sustainability. The Sustainability-Accuracy Index (SAI) balances detection accuracy, computational efficiency, and energy consumption. The Accurate Fault Detection and Responsiveness Index (AFDRI) incorporate responsiveness and false detection rates. Finally, the Sustainable and Accurate Fault Detection Index (SA-FDI) is presented as a composite indicator that measures both operational and sustainability performance. Additionally, an Intelligent Fault Detection Magic Square (IFDMS) is suggested as a decision-support visualization tool that allows partitioners to identify balance and prioritize improvements between technical and sustainability dimensions. This framework is validated using a lab-scale industrial robot. The results showed its effectiveness in highlighting the balance of technical and sustainability performance. This work contributes to life cycle thinking in SM, aligns with Sustainable Development Goals (SDGs), particularly SDG 9 (Industry, Innovation, and Infrastructure) and SDG 12 (Responsible Consumption and Production), and offers decision support for advancing sustainable and resilient industrial practices.