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.

In today's competitive economic environment, track and trace (the capability to determine present and past locations of products through the whole supply chain) is becoming an increasingly urgent necessity and a key differentiator in many industries, enhancing pilferage reduction, counterfeit prevention and targeted recalls, and improving supply chain efficiency, synchronization, visibility, and security. The pharmaceutical industry, together with the food industry, is one of the most advanced in traceability system implementation, as it has found to be strongly affected by the counterfeiting phenomenon. In this paper, a brief overview of traceability international regulations in the pharmaceutical sector and of the impact of its implementation on production processes and logistics will be continued, in order to highlight the main drivers of its large and fast diffusion. Technologies developed will also be presented and criticalities, and the benefits of traceability system implementation will be analysed.