In order to improve the availability of wind turbines\ud and to avoid catastrophic consequences, the detection of faults\ud in their earlier occurrence is fundamental. The paper proposes\ud the development of a fault diagnosis scheme relying on identified\ud fuzzy models. The fuzzy theory is exploited since it allows to approximate\ud uncertain models and manage noisy data. These fuzzy\ud models, in the form of Takagi–Sugeno prototypes, represent the\ud residual generators used for fault detection and isolation. A wind\ud turbine benchmark is used to validate the achieved performances\ud of the designed fault detection and isolation scheme. Finally,\ud extensive comparisons with different fault diagnosis methods\ud highlight the features of the suggested solution

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The fault diagnosis of wind farms has been proven to be a challenging task, and motivates the research activities carried out through this work. Therefore, this paper deals with the fault diagnosis of a wind park benchmark model, and it considers viable solutions to the problem of earlier fault detection and isolation. The design of the fault indicator involves data-driven approaches, as they can represent effective tools for coping with poor analytical knowledge of the system dynamics, noise, uncertainty, and disturbances. In particular, the proposed data-driven solutions rely on fuzzy models and neural networks that are used to describe the strongly nonlinear relationships between measurement and faults. The chosen architectures rely on nonlinear autoregressive with exogenous input models, as they can represent the dynamic evolution of the system over time. The developed fault diagnosis schemes are tested by means of a high-fidelity benchmark model that simulates the normal and the faulty behaviour of a wind farm installation. The achieved performances are also compared with those of a model-based approach relying on nonlinear differential geometry tools. Finally, a Monte-Carlo analysis validates the robustness and reliability of the proposed solutions against typical parameter uncertainties and disturbances.

Point of care ultrasonography and the related focused assessment with sonography for trauma protocol, if performed by experienced physicians, is a highly sensitive examination, and specific for the detection of free fluids. Different systems and methods have been proposed for training, including simulation as one of the most efficient. This paper presents an ultrasound training system, specifically designed to be used during bedside high fidelity simulation scenarios, that could facilitate the learning process. The development of the proposed system exploited novel rapid prototyping electronic boards as a means to obtain good performances with a low cost. Moreover, the design of the data structure permits the construction of a library that caters for individual needs, with the possibility of adding emergency scenarios, collecting pictures or videos, as well as 3-D volumes. The device has been compared with currently commercial ultrasound simulators and its innovative aspects have been highlighted. Finally, it has been tested during a training session in order to evaluate features, such as realism and user-friendliness.

The paper describes a control architecture for industrial\ud robotic applications allowing human/robot interactions, using\ud an admittance control scheme and direct sensing of the human inputs.\ud The aim of the proposed scheme is to support the operator\ud of an industrial robot, equipped with a force/torque (F/T) sensor\ud on the end-effector, during human/robot collaboration tasks involving\ud heavy payloads carried by the robot. In these practical\ud applications, the dynamics of the load may significatively affect\ud the measurements of the F/T sensor. Model-based compensation\ud of such dynamic effects requires to compute linear acceleration\ud and angular acceleration/velocity of the load, that in this paper\ud are estimated by means of a quaternion-based Kalman filter and\ud assuming that the only available measurements come from the forward\ud kinematics of the robot. Experimental results demonstrate\ud the feasibility of the approach and its industrial applicability