A Feature-Engineering Approach to Support Vector Machine-Based Damage Detection in Lead Zirconate Titanate Ceramics Via Point-Contact Wavefield Measurement

In this study, a machine learning-based detection and localization of localized damage in lead zirconate titanate (PZT) ceramics is developed. A point-contact excitation and detection method is employed to excite and detect acoustic wave signals from the PZT sensor. The signals are analyzed using non-destructive evaluation techniques. The significant features of wavelet transform coefficients, auto-regressive modeling parameters, peak amplitude, peak location, and wave energy are extracted from the waveforms. These features capture the salient properties of the acoustic response that change in the presence of structural damage. A trained support vector machine classifier is used to distinguish between damaged and healthy regions based on the extracted features. Classification achieved a recall of 92.7% and a precision of 86.0% for the minority damaged class. However, the method is compromised at the center of the samples, where the wave energy is the highest and the signal originates. Furthermore, the thresholding method used in data labeling can be sensitive to local anomalies, potentially leading to misclassification. Despite these challenges, the proposed framework supports a scalable and robust real-time damage detection system. By integrating machine learning, point-contact acoustic sensing, and signal processing, this study contributes to the development of automated and accurate structural health monitoring techniques for smart sensing systems.