An Ultra-Compliant and Flexible Structural Sensing Neural System for Damage Detection in Wind Turbine Blades
Published in ASME 2021 International Mechanical Engineering Congress and Exposition, 2021
Recommended citation: Wang, J., Shen, Y., Peng, X., Han, Z., & Jiang, S. (2021). An Ultra-Compliant and Flexible Structural Sensing Neural System for Damage Detection in Wind Turbine Blades. In Volume 7A: Dynamics, Vibration, and Control. https://doi.org/10.1115/imece2021-70986. https://doi.org/10.1115/IMECE2021-70986
This paper presents an ultra-compliant and flexible structural sensing neural system for damage detection in wind turbine blades.
During my time as a Research Assistant and Assistant Lab Manager at the Active Material and Intelligent Structures Lab (Dec 2019 - Sept 2021), I played a crucial role in the development of an ultra-compliant and flexible sensing system using convolutional neural networks (CNNs) for damage detection in wind turbine blades. My primary contributions included fabricating the neural skin by coating piezoelectric sensing powder on flexible printed circuits, assisting in the polarization of composite piezoelectric materials, and applying CNNs to the sensing system for predictive pulse signal detection. Our research was successfully published in the ASME 2021 International Mechanical Engineering Congress and Exposition (IMECE) proceedings.