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Classification of damages in honeycomb composite sandwich structure (HCSS) isimportant to decide remedial actions. However, previous studies have onlydetected damages using deviations of monitoring signal from healthy (baseline)using a guided wave (GW) based structural health monitoring system.Classification between various types of damages has not been reported forchallenging cases. We show that using careful feature engineering and machinelearning it is possible to classify between various types of damages such ascore crush (CC), high density core (HDC), lost film adhesive (LFA) and teflonrelease film (TRF). We believe that we are the first to report numerical modelsfor four types of damages in HCSS, which is followed up with experimentalvalidation. We found that two out of four damages affect the GW signal in aparticularly similar manner. We extracted and evaluated multiple features fromtime as well as frequency domains, and also experimented with features relativeto as baseline as well as those that were baseline-free. Using Pearson'scorrelation coefficient based filtering, redundant features were eliminated.Finally, using an optimal feature set determined using feature elimination,high accuracy was achieved with a random forest classifier on held-out signals.For evaluating performance of the proposed method for different damage sizes,we used simulated data obtained from extensive parametric studies and got anaccuracy of 77.89%. Interpretability studies to determine importance of variousfeatures showed that features computed using the baseline signal prove moreeffective as compared to baseline-free features.

Classification of Various Types of Damages in Honeycomb Composite Sandwich Structures using Guided Wave Structural Health Monitoring