Wind turbines are increasing their generation energy capacity, which requires an increase in reliability. One of the most relevant failures that occur in wind turbines is the one in the main bearing. The study of methods that allows predicting the behavior of Wind Turbines under different operation conditions will permit a forecast of any possible failure in the elements of the device. The current work presents the first results in developing a wind turbine digital twin using SCADA data. This DT should allow to estimate and simulate the behavior of the WT and be an additional tool in the prediction of failures. The used platform is MATLAB and its Simulink and Simscape modules.

In the current meso cutting technology industry, the demand for more advanced, accurate and cheaper devices capable of creating a wide range surfaces and geometries is rising. To fulfill this demand, an alternative single point cutting device with 6 degrees of freedom (6DOF) was developed. Its main advantage compared to milling has been the need for simpler cutting tools that require an easier development. To obtain accurate and precise geometries, the tool tip must be monitored to compensate its position and make the proper corrections on the computer numerical control (CNC). For this, a stereo vision system was carried out as a different approach to the modern available technologies in the industry. In this paper, the artificial intelligence technologies required for implementing such vision system are explored and discussed. The vision system was compared with commercial measurement software Dino Capture, and a dedicated metrological microscope system TESA V-200GL. Experimental analysis were carried out and results were measured in terms of accuracy. The proposed vision system yielded an error equal to ±3 µm in the measurement.

Despite the rise of robotics and automation in industrial applications, the widespread adoption of collaborative robotics still needs to be improved due to the lack of interoperability between robots and the low adaptability of existing systems. Solving this problem would mean a significant advance in robotics and industrial automation. Under this context, an open-source MultiRobot Framework based on ROS2 was developed in the present research to effectively communicate and coordinate robotics agents and sensors in closed collaborative environments. A simulation-based control and software design was performed using the Gazebo tool.Acentralized architecturewas obtained with an autonomous navigation module for the planning and robot routes monitoring, a computer vision module for the location and management of uncertainties, and a task controller module to assign mobilization mission objects. In conclusion, using ROS2 to communicate and coordinate various mechatronic systems effectively results in a robust, flexible, and scalable solution critical to industrial processes.

The rapid growth of the energy capacity generated by wind turbines, as well as their size, creates new challenges in terms of operation and maintenance. Main bearing failures have been identified as one of the main and most critical issues in terms of increasing the availability and reliability of wind turbines. Therefore, predicting wind turbine main bearing failures has become a technical challenge and an economically important issue. In this manuscript it is established a fault prognosis procedure to forecast main bearing failures several months beforehand to help wind turbine operators plan maintenance in advance. Reducing downtime is one of the main goals in wind energy industry since it lets addressing its energy loss impact. The main contributions of this work are described below: i) the prognosis is carried out by using only SCADA data, which is accessible in all commercial wind turbines; therefore, no additional sensors designed for a determined purpose need to be installed; ii) it is a semi-supervised approach, thus it only requires healthy data to be collected; iii) it is based only on external variables and one representative temperature close to the subsystem under diagnosis, thus avoiding data contamination; iv) it accomplishes the prognosis (several months beforehand) of the main bearing fault; and v) the performance and validity of the proposed strategy are demonstrated on a real underproduction wind turbine.