Early warning of damaged wind turbine blades using spatial–temporal spectral analysis of acoustic emission signals

A spatial–temporal processing framework is proposed to forecast the wind turbine blade damage in the early stage. The sparse Bayesian learning beamforming (SBL) is applied to data received by a microphone array for enhancement of weak signals and suppressing interference of environmental noise. Then short-time Fourier transform (STFT) is utilized to create a time–frequency spectrum and analyze the nonstationarity of acoustic emission signals. The period of radiation energy change and the cyclic modulation spectrum (CMS) are respectively calculated from the time–frequency spectrum. Blade fault detection is performed based on whether or not the presence of the periodicity or cyclostationary signatures in acoustic emission signals. Numerical simulations have shown that the natural frequencies of acoustic emission signals tend to decrease when there is a hole on the blade surface. The experimental results have verified the effectiveness and robustness of the proposed blade damage detection method. •More energy is emitted by cracks on the blade tip. Natural frequencies tend to decrease.•Cyclostationary and periodical characteristics are utilized for detection of blade faults.•The blade damage forecast approach is validated by tests using data from wind power plants.