## 4 Damage Detection Models Using Ultrasonic Guided Waves for SHM in  
**Laminated Composites**  
Data detection in the ultrasonic SHM of composite structures involves several critical steps to ensure the precise detection and characterization of potential defects. The process commences with the deployment of sensors, typically piezoelectric transducers, which are permanently affixed to the structure. These sensors emit and receive guided ultrasonic waves that travel through the material. The interaction of these waves with structural anomalies, such as cracks or delaminations, modifies their propagation characteristics, which are subsequently captured by the sensors. The data acquisition system precisely controls the timing and management of ultrasonic wave generation and reception. Field Programmable Gate Arrays (FPGAs) are commonly used in modern systems for real-time data processing and acquisition due to their flexibility and high-speed capability [118]. The signals are digitized and stored for subsequent analysis. To extract valuable information from the raw ultrasonic data, signal processing techniques are employed. These include filtering to eliminate noise, normalizing the signal, and extracting features. Emerging ML algorithms and probabilistic imaging techniques are transforming SHM [119]. CNNs excel in automatically identifying complex damage patterns from ultrasonic data, while SVMs effectively classify damage types and estimate severity with minimal training data. Complementing these, probabilistic imaging methods like Bayesian inference enhance  
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SHM systems by quantifying uncertainties in damage localization and mapping wave propagation data into probabilistic damage images with confidence intervals. This synergy improves localization accuracy and enables predictive maintenance by tracking damage progression under operational conditions. Figure 9 illustrates the diagnostic imaging procedure using ultrasonic guided wave detection technology for a damaged composite material. This includes four steps: initially, experiments are conducted on a test bench and raw signals from GWs are collected; then, the received signals are compressed using an advanced piecewise aggregate approximation (PAA) algorithm, through which a new time series is acquired after multi-path fusion, and processed to yield a two-dimensional image sample preserving temporal feature information; next, a sample set is generated using a combination of 2D images and mapping the damage coordinates into the training, testing, and validation sets; and finally, damage location is identified based on the output, and model performance is assessed using various metrics. Signal processing is crucial for interpreting data collected by ultrasonic sensors for damage detection. The complexity of the signals from composite structures necessitates sophisticated processing techniques to distinguish between different types of anomalies and noise. Fundamental techniques in ultrasonic signal processing include the use of WT and Short Time Fourier Transform (STFT) to analyze the signals' time-frequency properties. These methods facilitate the identification of transient events and defect localization within the structure. Figure 9: Damage localization flowchart using the GAF–CNN–CBAM model for damage localization. Reprinted with permission from ref. [120]. Machine learning has significantly advanced various fields, including its application in ultrasonic NDT for the SHM of composite structures. ML algorithms efficiently process and analyze vast volumes of data with high accuracy, thereby enhancing damage detection and characterization capabilities. These algorithms improve the ability to detect, classify, and predict damage by processing extensive ultrasonic data accurately and efficiently.