An Ultra-Compliant and Flexible Structural Sensing Neural System for Damage Detection in Wind Turbine Blades

Abstract

This paper presents a new ultra-compliant and flexible structural sensing neural system for damage detection in wind turbine blades. The entire sensing cluster is integrated inside a flexible printed circuit (FPC), which complies the structural geometric features as a neural skin planted on the structural surfaces. It is worth of noting that the proposed sensing system can be mounted on arbitrary locations of a wind turbine blade, mimicking the neurons of the human biological system to detect and monitor the damage in the blade. Besides, all the sensing elements are interfaced with a laptop-controlled data acquisition board to receive the structural dynamic responses. In addition, a miniature electromagnetic actuator is utilized to excite the blade. Additional mass is employed to simulate the damage situation. Several signal processing techniques are implemented to analyze the oscillatory responses, such as wavelet analysis, fast Fourier transform (FFT), and short time Fourier transform (STFT). Furthermore, the damage indices (DIs) which correlate the structural spectral power density of both pristine and damaged cases can accurately identify the location of damage. Such a sensing neural system possesses the tremendous potential in future Structural Health Monitoring (SHM) and Nondestructive Evaluation (NDE) applications. This paper finishes with discussion, concluding remarks, and suggestions for future work.