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<p>The structure of the wind turbines nowadays is a critical element due to their importance from the reliability, availability, safety, and cost points of view. This is more relevant when the offshore wind turbine is considered. This paper introduces a novel design of a Fault Detection and Diagnosis (FDD) model based on ultrasound technique. The FDD model will be able to detect fault/failures via the pulse-echo technique. The pulse-echo is got via piezoelectric transducers that are also employed as sensors. The signal processing is based on two steps. Firstly, a wavelet transform is applied to the measured signals with filtering purposes, in order to enhance the signal to noise ratio. Secondly, a time series modeling approach, as an autoregressive with exogenous input model, is employed for pattern recognition by minimizing the Akaike information criterion. An experimental platform is proposed to test the procedure, where pulse-echo experiments were employed before and after a fault occurred. The results from this paper lead to the identification of an early indication of structural problems induced by internal (material, shape, age, etc.) and external (temperature, humidity, pressure, etc.) factors. The model can anticipate catastrophic faults, reducing the preventive/corrective tasks and costs, etc, and increasing the availability of the wind turbine, and therefore the energy production.</p>
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Machine-Learning-Based Intelligent Mechanical Fault Detection and Diagnosis of Wind Turbines
