Browsing by Author "Jimenez Cortadi, Alberto"
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Item Condition monitoring of wind turbine pitch controller: A maintenance approach: A maintenance approach(2018-07) González-González, Asier; Jimenez Cortadi, Alberto; Galar, Diego; Ciani, Lorenzo; Tecnalia Research & Innovation; IAWith the increase of wind power capacity worldwide, researchers are focusing their attention on the operation and maintenance of wind turbines. A proper pitch controller must be designed to extend the life cycle of a wind turbine’s blades and tower. The pitch control system has two primaries, but conflicting, objectives: to maximize the wind energy captured and converted into electrical energy and to minimize fatigue and mechanical load. Four metrics have been proposed to balance these two objectives. Also, diverse pitch controller strategies are proposed in this paper to evaluate these objectives. This paper proposes a novel metrics approach to achieve the conflicting objectives with a maintenance focus. It uses a 100 kW wind turbine as a case study to simulate the proposed pitch control strategies and evaluate with the metrics proposed. The results are shown in two tables due to two different wind models are used.Item A statistical data-based approach to instability detection and wear prediction in radial turning processes(2018) Jimenez Cortadi, Alberto; Irigoien, Itziar; Boto, Fernando; Sierra, Basilio; Suarez, Alfredo; Galar, Diego; Tecnalia Research & Innovation; FACTORY; FABRIC_INTELRadial turning forces for tool-life improvements are studied, with the emphasis on predictive rather than preventive maintenance. A tool for wear prediction in various experimental settings of instability is proposed through the application of two statistical approaches to process data on tool-wear during turning processes: three sigma edit rule analysis and Principal Component Analysis (PCA). A Linear Mixed Model (LMM) is applied for wear prediction. These statistical approaches to instability detection generate results of acceptable accuracy for delivering expert opinion. They may be used for on-line monitoring to improve the processing of different materials. The LMM predicted significant differences for tool wear when turning different alloys and with different lubrication systems. It also predicted the degree to which the turning process could be extended while conserving stability. Finally, it should be mentioned that tool force in contact with the material was not considered to be an important input variable for the model.