“Vibration-based tools for the optimisation of large-scale industrial wind turbines devices”

Abstract

Wind turbines (WT) maintenance management must be in continuous improvement to develop reliability, availability, maintainability and safety (RAMS) programs, and to achieve time and cost reductions in large-scale industrial wind turbines. The optimisation of the operation reliability involves the supervisory control and data acquisition to guarantee these correct levels of RAMS. A fault detection and diagnosis methodology (FDD) is proposed for mechanical devices of a WT. The method applies the wavelet and Fourier analysis to vibration signals. The signals collected contain information on failures found in the gearbox-generator set. The information is initially tested by the fast Fourier transform (FFT) to ensure the accuracy of the information. Then, a pattern based on energies that relates each failure with different frequency bands is created. This pattern uses the wavelet transform as the main technique. A number of turbines of the same type were instrumented in the same wind farm. The data collected from the individual turbines was fused and analysed together in order to determine the overall performance. It is expected that data fusion allow a significant improvement since the information gained from various condition monitoring systems can be enhanced. Effort will also focus on the application of dependable embedded computer systems for a reliable implementation.