This paper introduces a novel Fault Detection and Diagnosis method based on the wavelet transform to detect defects on the tower of a wind turbine. 24 Macro-Fiber Composite transducers have been placed to send and collect ultrasound signals. The data have been converted into voltage and analysed by the wavelet transforms. Wavelet transform detects particular characteristics according to the shape or amplitude, and lead to diagnose imperfections in towers. Regarding to the wind turbines maintenance management, a large number of publications can be found focused on blades, mechanical, electrical/electronic devices, etc. However this trend is not extended to the wind turbine towers, and the development of their maintenance systems is not widespread yet. The results from this paper lead to acquire an early indication of structural or mechanical problems and it anticipates to catastrophic failures. It also allows a better preventive and predictive maintenance in wind turbines.

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This paper presents a novel pattern recognition approach for a non-destructive test based on macro fibre composite transducers applied in pipes. A fault detection and diagnosis (FDD) method is employed to extract relevant information from ultrasound signals by wavelet decomposition technique. Wavelet transform is a powerful tool that reveals particular characteristics as trends or breakdown points. The FDD developed for the case study provides information about the temperatures on the surfaces of the pipe, leading to monitor faults associated to cracks, leaks or corrosion. This issue may not be noticeable when temperatures are not subject to sudden changes, but it can cause structural problems in the medium and long-term. Furthermore, the case study is completed by a statistical method based on the coefficient of determination. The main purpose will be to predict future behaviours in order to set alarms levels as part of a structural health monitoring system.

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Wind turbines (WT) maintenance management is in continuous development to improve the reliability, availability, maintainability and safety (RAMS) of WTs, and to achieve time and cost reductions. The optimisation of the operation reliability involves the supervisory control and data acquisition to guarantee correct levels of RAMS. A fault detection and diagnosis methodology is proposed for large-scale industrial WTs. The method applies the wavelet and Fourier analysis to vibration signals. A number of turbines (up to 3) of the same type will be instrumented in the same wind farm. The data collected from the individual turbines will be fused and analysed together in order to determine the overall reliability of this particular wind farm and wind turbine type. It is expected that data fusion will allow a significant improvement in overall reliability since the value of the information gained from the various condition monitoring systems will be enhanced. Effort will also focus on the successful application of dependable embedded computer systems for the reliable implementation of wind turbine condition monitoring and control technologies.