Ultrasonic guided wave-based probabilistic diagnostic imaging method with Single-Path-Scattering sparse reconstruction for Multi-Damage detection in composite structures

• Published in: Mechanical systems and signal processing 2025-01, Vol.223, p.111858, Article 111858
• Main Authors: Sun, Lingyu, Wei, Juntao, Peng, Chang, Hao, Wei, Teng, Feiyu, Fan, Longqing, Zhang, Lei, Sui, Qingmei, Jiang, Mingshun
• Format: Article
• Language: English
• Subjects: Multi-damage detection; Probabilistic diagnostic imaging; Sparse reconstruction; Ultrasonic guided waves
• ISSN: 0888-3270
• DOI: 10.1016/j.ymssp.2024.111858

Plate-like carbon fiber composite structures are essential for equipment such as spacecraft and rail vehicles. These structures are exposed to long-term cyclic loads and harsh environmental conditions, leading to damage of unknown location and quantity. Ultrasonic guided wave (UGW) detection technology shows promise for evaluating the structural performance of composite structures due to its advantages of high precision, high speed, and real-time capabilities. However, multi-damage scattering sources interfere with and superimpose the received signals when multiple damages coincide. This interference can lead to artifacts in probability imaging methods that rely on features such as amplitude and time of flight (TOF). This paper proposes a single-path-scattering sparse reconstruction-based probabilistic diagnostic imaging (SSR-PDI) method. Firstly, a Lamb dictionary with time shift is constructed. Each actuator-receiver path is treated as a target signal, enabling the approximate separation and extraction of multiple scattering waves from different damages. It mitigates the “curse of dimensionality” associated with the sparse reconstruction (SR) method. Secondly, a new ring-shaped damage probability distribution is proposed based on the sparse representation matrix’s time mapping and amplitude weighting. The experimental results demonstrate that the SSR-PDI method effectively mitigates issues related to multi-damage detection, specifically false negatives/positives and localization errors arising from the superposition of scattering sources.