28 Mar 2025

The University of Ferrara, through the laboratory IN4 Hub for EngINering INnovation and INdustry INtegration, coordinates the REFIMAN project—Predictive Maintenance Platform for Sustainable Retrofitting of Existing Plants and Machinery in the Emilia-Romagna Production System—funded by the Emilia-Romagna Region under the PR FESR program. The project is carried out in close synergy with three other partners: InterMech–MO.RE., SITEIA.Parma, and TekneHub, laboratories belonging to the Universities of Modena and Reggio Emilia, the University of Parma, and the University of Ferrara, respectively.

The project aims to develop methodologies and technical tools to radically transform the maintenance of production plants predating the digital transition. The innovation of the regional production system is pursued through the development and application of advanced technologies, creating a predictive maintenance platform that integrates sophisticated hardware with machine learning-based software. This system will transform pre-Industry 4.0 plants by improving the diagnosis and prognosis of mechatronic components, extending the lifespan of equipment, and optimizing maintenance plans. REFIMAN's impact extends beyond technological advancements, promoting a sustainable production model aligned with the environmental goals of the Emilia-Romagna Region.

The retrofitting system is based on diagnostic and prognostic techniques combined with machine learning algorithms, making it adaptable to a wide range of industrial plants and capable of refining its estimates over time.

The project also involves several companies from different sectors—FAVA S.P.A., I.M.A. Industria Macchine Automatiche S.P.A., OBER S.P.A., and Parma Food Group s.r.l.—each of which will integrate the REFIMAN platform at the prototype level in their industrial plants. These companies actively participate in testing and development phases, contributing their specific requirements and facilitating the system’s engineering.

The contribution of the IN4 laboratory is crucial to the project's success. With its expertise in advanced sensor technology and data analysis, the IN4 team develops algorithms that predict machine degradation, enabling timely maintenance interventions and preventing major failures.

The preliminary results indicate a significant increase in efficiency and a reduction in maintenance costs, thus supporting a more sustainable production model. For example, in the case of an automatic machine produced by IMA for the pharmaceutical field, the REFIMAN platform detects and analyzes the forces at play to monitor and optimize the lubrication status of mechanical components. Some of the most innovative analysis techniques are used, including a specially trained neural network, designed to detect faults promptly through continuous error analysis, an example of which is shown in the accompanying time-series diagram.

REFIMAN thus represents a model of how technological innovation can be applied to enhance industrial efficiency, reduce environmental impact, and promote sustainability, marking a significant step toward achieving Industry 4.0 objectives.