Wind energy has shown significant growth in terms of installed power in the last decade. However, one of the most critical problems for a wind farm is represented by Operation and Maintenance (O&M) costs, which can represent 20–30% of the total costs related to power generation. Various monitoring methodologies targeted to the identification of faults, such as vibration analysis or analysis of oils, are often used. However, they have the main disadvantage of involving additional costs as they usually entail the installation of other sensors to provide real-time control of the system. In this paper, we propose a methodology based on machine learning techniques using data from SCADA systems (Supervisory Control and Data Acquisition). Since these systems are generally already implemented on most wind turbines, they provide a large amount of data without requiring extra sensors. In particular, we developed models using Artificial Neural Networks (ANN) to characterize the behavior of some of the main components of the wind turbine, such as gearbox and generator, and predict operating anomalies. The proposed method is tested on real wind turbines in Italy to verify its effectiveness and applicability, and it was demonstrated to be able to provide significant help for the maintenance of a wind farm.

The Fourth Industrial Revolution, known as Industry 4.0, has transformed the manufacturing landscape by integrating advanced digital technologies, fostering automation, interconnectivity, and data-driven decision-making. Among these innovations, Digital Twins (DTs) have emerged as a pivotal tool, enabling real-time monitoring, simulation, and optimization of production processes. This paper provides a comprehensive exploration of DT technology, offering a strategic framework for its effective implementation within Industry 4.0 environments to enhance operational efficiency. The proposed methodology integrates key enabling technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning to create accurate digital replicas of manufacturing systems. Through a detailed case study, this work demonstrates how DTs can optimize production processes, reduce downtime, and improve maintenance strategies. The findings highlight DTs’ transformative potential in achieving continuous improvement, competitiveness, and operational excellence. This research aims to provide organizations with actionable insights and a roadmap to leverage DT technology for sustainable industrial innovation.

Most manufacturing and process industries require compressed air to such an extent that in Europe, for instance, about 10% of the total electrical energy consumption of industries is due to compressed air systems (CAS). However, energy efficiency in compressed air production and handling is often ignored or underestimated, mainly because of the lack of awareness about its energy consumption, caused by the absence of proper measurements on CAS in most industrial plants. Therefore, any effective energy saving intervention on generation, distribution and transformation of compressed air requires proper energy information management. In this paper we demonstrate the importance of monitoring and controlling energy performance in compressed air generation and use, to enable energy saving practices, to enhance the outcomes of energy management projects, and to obtain additional benefits for non-energy-related activities, such as operations, maintenance management and energy accounting. In particular, we propose a novel methodology based on measured data, and baseline definition through statistical modelling and control charts. The proposed methodology is tested on a real compressed air system of a pharmaceutical manufacturing plant in order to verify its effectiveness and applicability.

Industry 4.0 and Industry 5.0 have introduced a lot of innovative technologies in industrial plants, transforming them into complex digital systems. On the other hand, the importance of Energy Management Systems in industrial plants is growing for both sustainability and economic reasons, but the opportunity of Industry 4.0/5.0 technologies in enhancing energy management systems is not fully understood. Thus, this paper analyzes how Industry 4.0/5.0 technologies can be applied to meet the requirements of Energy Management Systems, focusing on each aspect such as design, monitoring, control, and budget planning. It identifies additional opportunities that arise with different levels of technological implementation, suggesting organic implementation steps. The final aim is to provide a comprehensive framework for fostering a strategic and conscious implementation approach of these tools in the Energy Management Systems of industrial plants, giving clear and comprehensive suggestions.

In the age of digital transformation, maintenance operations are crucial for leveraging the potential of Industry 4.0 and 5.0. Yet, this domain remains significantly under-optimized in terms of strategic maintenance planning and enhancing asset performance. The advent of smart technologies offers a myriad of innovative avenues; however, harnessing these effectively requires systematic planning that incorporates these new, various and quite diversified, smart practices. Thus, this paper proposes a new methodological approach to maintenance planning, based on the Reliability-Centered Maintenance method, aimed at providing an operative tool for organizations to foster the evolution of their maintenance plans towards the paradigm of digitalization. This novel method enables the identification of hidden opportunities of improvement not identifiable through the use of the traditional approach through the proposal of an Opportunity Index, to use together with the Criticality Index in asset selection, and a Digitalization Score to use during Failure Mode, Effects, and Criticality Analysis. The proposed method is applied to transform the maintenance planning of a production line, thus identifying the opportunities of the approach and testing its feasibility.