Acoustic Emission Monitoring of Steel Joints Using Cepstral Coefficients
Vibration-based Structural Health Monitoring (SHM) techniques are effective for identifying global dynamic behavior, but local damage detection and source localization remain challenging, particularly when damage initiates at small scales or in confined joint regions. In steel structures, critical deterioration mechanisms often develop at welded or mechanically connected details, where modal vibration signatures can remain largely unaltered during early damage stages, reducing their diagnostic sensitivity. This limitation motivates the integration of monitoring approaches that are driven directly by material-level response. Acoustic Emissions (AE) provide a non-destructive and high-sensitivity monitoring strategy generated by transient elastic waves released from a material when mechanically stressed. AE events originate at the damage source itself, enabling a direct assessment of local condition variations in structural details and joints. AE monitoring is therefore particularly suited for steel connections, where deterioration may remain undetected by global dynamic metrics. Current AE monitoring approaches often rely on elementary signal descriptors—including peak amplitude and waveform duration—which are widely reported in the literature as indicators of critical structural states. On the other hand, dominant frequency tracking can be ambiguous to extract reliably due to noise sensitivity, mode mixing, and signal non-stationarity. To address this challenge, the present work explores the use of Cepstral Coefficients (CC) computed from AE waveforms as a stable and straightforward feature for signal characterization. Unlike spectral peaks, CC are directly and uniquely derived from the input signal through a computationally lightweight procedure. These characteristics make CC attractive for rapid laboratory assessment and future SHM integration. In this context, an experimental study was carried out on welded, full-penetration steel joint specimens tested at University of Trento. The specimens, instrumented with AE sensors, were tested at the University of Trento under controlled low-cycle loading protocols inducing yielding damage. Based on the controlled load history, a comprehensive dataset of AE signals was collected from both undamaged and damaged specimens to assess how local deterioration alters CC features, which were extracted from raw AE waveforms and statistically compared between undamaged and damaged conditions. The analysis highlights differences in CC distributions, showing potential in discriminating between distinct structural damage states in steel joints without requiring complex frequency identification. The results support the CC-based characterization of AE signals as a complementary monitoring layer for SHM systems targeting critical steel joint regions.