Predictive maintenance in Industry 4.0: A systematic multi-sector mapping

Industry 4.0 is strongly intertwined with big data streaming flows from intelligent sensors and machinery installed in industrial facilities. Failures can disrupt production and lead the supply chain ecosystem to malfunction. Maintenance strategies are necessary to safeguard the continuous operation of production lines, minimize supply chain disruptions, and improve sustainability indicators. Within the context of smart manufacturing, predictive maintenance (PdM) approaches could decrease downtimes, reduce operational costs, and increase productivity, improving system performance and decision-making. The overarching aim of this research is to systematically review state-of-the-art predictive maintenance applications across diverse manufacturing sectors to provide customized insights from academic and operational perspectives, summarized into a comparative decision support map. The study classifies predictive maintenance solutions based on prevailing methodologies, input features, predicted variables, applied algorithms, evaluation metrics, and state-ofthe-art software tools per industry sector. The outcomes highlight that data-driven predictive maintenance constitutes a cutting-edge solution with a growing interest in modern manufacturing. Moreover, this research provides insights into the technology readiness of each industrial sector, covering modern areas for PdM implementation, while raising the extant challenges. The proposed multi-sector framework is expected to act as a guiding light for researchers and practitioners towards the development of PdM driven applications in data driven industries.