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The safety and integrity requirements of aerospace composite structures necessitate real-time health monitoring throughout their service life. To this end, distributed optical fiber sensors utilizing back Rayleigh scattering have been extensively deployed in structural health monitoring due to their advantages, such as lightweight and ease of embedding. However, identifying the precise location of damage from the optical fiber signals remains a critical challenge. In this paper, a novel approach which namely Modified Sliding Window Principal Component Analysis (MSWPCA) was proposed to facilitate automatic damage identification and localization via distributed optical fiber sensors. The proposed method is able to extract signal characteristics interfered by measurement noise to improve the accuracy of damage detection. Specifically, we applied the MSWPCA method to monitor and analyze the debonding propagation process in honeycomb sandwich panel structures. Our findings demonstrate that the training model exhibits high precision in detecting the location and size of honeycomb debonding, thereby facilitating reliable and efficient online assessment of the structural health state.

A Modified Principal Component Analysis Method for Honeycomb Sandwich Panel Debonding Recognition Based on Distributed Optical Fiber Sensing Signals