A multi-information fusion method for lamb-wave-based damage imaging in orthotropic CFRP laminates using a single self-sensing PZT

Carbon fiber reinforced polymers (CFRPs) are extensively used in the manufacturing of ships, aircraft, and land vehicles. Conventional structural health monitoring approaches typically rely on dense sensor networks, which significantly increase system complexity and cost. To address this issue, this paper proposes a multi-information fusion framework for precise damage localization in orthotropic CFRP laminates using only a single self-sensing transducer. A dedicated integrated excitation–sensing circuit model, combining diode clamping with capacitive voltage division, is first developed to allow the same PZT element to sequentially transmit high-voltage Lamb waves and capture low-amplitude response signals. On this basis, direct damage-scattered wave components extracted from multi-frequency signals are fused with the first multi-path damage-scattered wave packet in low-frequency signals, yielding the initial damage position estimate. To resolve the origin ambiguity of boundary-reflected wave packets, a binary masking scheme is introduced. Complementary time-of-flight masks suppress artifacts from incorrect boundary assumptions, and superposition identifies the damage as the maximum-intensity pixel. Experimental validation on an orthotropic CFRP laminate and a wind turbine blade with the crack damage confirms that the proposed method can detect and accurately localize damage using only a single self-sensing PZT.