Wavelet transforms and pattern recognition on ultrasonic guides waves for frozen surface state diagnosis

Icing blades require of advanced condition monitoring systems to reduce the failures and downtimes in Wind Turbine Blades (WTB). This paper presents a novel fault detection and diagnosis system that combines ultrasonic techniques with Wavelet transforms for detecting ice on the blades. Lamb waves were generated with Macro Fibre Composites (MFC) and collected with MFC. Ice affects to the normal propagation of the wave through the material of the blade. The changes in the signal are due to the forces that ice exercise on the surface. Three different scenarios were considered according to ISO 12494, 2001 (Atmospheric icing of structures): at room temperature; the frozen blade without accumulation of ice, and; the frozen blade with accumulation of ice on its surface. In order to validate the approach, Morlet wavelet transformation has been used for filtering the signal. The time-frequency analysis has been done by Wigner-Ville distribution. On the other hand, the envelope of the filtered signal by wavelet transforms is done by Hilbert Transform, and the pattern recognition is done by autocorrelations of the Hibert transforms. The approach detects the cases considered in ISO 12494 of unfrozen, frozen without ice, and frozen with ice in the WTB. New scenarios, considering mud, have been considered to test the approach.