Toward an operational predictive maintenance 5.0 framework informed by a scoping review and an industrial case study

The transition toward Industry 5.0 (I5.0) is reshaping industrial systems around sustainability, Human-Centricity (HC), and resilience, yet most Predictive Maintenance (PdM) approaches remain focused on technical objectives inherited from Industry 4.0. To characterize the maturity of I5.0-aligned PdM research, a scoping review of the Scopus database was conducted, screening 238 articles and retaining 123 based on substantive-engagement criteria, followed by a weighted scoring protocol assessing the depth of engagement with the three I5.0 pillars. The review reveals a pronounced skew toward shallow integration, with only a minority of studies operationalizing sustainability, HC, and resilience jointly. Building on these findings, this paper proposes an implementation-oriented Predictive Maintenance 5.0 (PdM 5.0) framework structured around three interconnected layers. The framework introduces a structured Large Language Model-mediated human-in-the-loop interaction, an adaptive learning mechanism, and an energy and CO 2 estimation pathway linking equipment degradation to sustainability indicators. A preliminary industrial case study using operational data from a recycled-paper mill demonstrates the practical execution of three interconnected pathways of the framework: equipment health assessment, energy and CO 2 impact estimation, and human-in-the-loop interaction supported by LLM-mediated explanations. Layer-specific evaluation metrics are further proposed to support broader operationalization. This work contributes a scoping-review-grounded architecture that advances PdM from a purely technical function toward a holistic, I5.0-aligned paradigm, while providing initial empirical evidence of its operational feasibility and a structured basis for broader industrial validation.