Novel structural health monitoring method for CFRPs using electrical resistance based probabilistic sensing cloud

Abstract

We propose the probabilistic sensing cloud method for non-destructive self-sensing impact localization in carbon fiber reinforced plastics (CFRPs) with optimized electrode arrays. Electrical resistance was measured between various electrode sets to identify the potential damage area. Subsequently, overlapped probabilistic clouds helped localize the damaged location, which was verified by our experimental results. The proposed technique was optimized by investigating the inter-electrode distance, finite element analysis of electrical current density, and cloud shaping in terms of the resistance change. Pre-existing techniques such as eddy current sensing, fiber Bragg grating sensing, and lead zirconate titanate sensing are limited to schedule-based inspection or sparse sensing units holding blind spots. However, the proposed method is an in situ real-time condition-based self-sensing method that requires no additional sensors and fewer electrodes. Furthermore, the noise and error components for the structure were significantly lower than in ordinary piezoresistive self-sensing systems. Therefore, probabilistic sensing cloud method can enhance efficient structural health monitoring of CFRPs with electrode distance optimization and can reduce data complexity induced by structural complexity.