This article studies experimentally the damage behaviors of a 59.5-m-long composite wind turbine blade under accelerated fatigue loads using acoustic emission technique. First, the spectral analysis using the fast Fourier transform is used to study the components of acoustic emission signals. Then, three important objectives including the attenuation behaviors of acoustic emission waves, the arrangement of sensors as well as the detection and positioning of defect sources in the composite blade by developing the time-difference method among different acoustic emission sensors are successfully reached. Furthermore, the clustering analysis using the bisecting K-means method is performed to identify different damage modes for acoustic emission signal sources. This work provides a theoretical and technique support for safety precaution and maintaining of in-service blades.

Magnetocardiography (MCG) plays a growing role in non-invasive cardiac disease diagnosis. However, MCG signals are prone to environmental magnetic fields and metal artifacts, distorting waveforms and affecting diagnostic accuracy. Existing methods like the Fast Independent Component Analysis (FastICA) and Information Maximization (Infomax) algorithm have limitations in suppressing ultra-low frequency metal artifacts. We propose a Second-Order Blind Identification algorithm based on an optimized time-delay matrix, utilizing temporal coherence to effectively separate ultra-low frequency metal artifacts from mixed sources. An automatic screening method for metal artifacts, QRS, T/P waves, and unknown interferences is established using time-frequency features. Extensive simulations and real OPM-MCG experiments validate our method’s superiority in metal artifact suppression. The results show our method surpasses FastICA and Infomax in suppressing metal artifacts, achieving average SNR improvement of 5.36% to 29.40% across four subjects. Reconstructed P/QRS/T waves are undistorted, with a minimum 80.71% reduction in RMSE. This method potentially expands MCG’s clinical applications, benefiting more patients.