A novel enhanced Superlet Synchroextracting transform ensemble learning for structural health monitoring using nonlinear wave modulation

Abstract

This study investigates the application of nonlinear wave modulation (NWM) using chirp signals for structural health monitoring (SHM). The implementation of NWM with monoharmonic signals (periodic signals that consist of a single frequency component) poses significant challenges due to the complexity of selecting optimal pump and carrier frequencies, leading to time-intensive processes. In contrast, analyzing NWM with chirp signals introduces additional complexities regarding signal processing compared to monoharmonic excitations. Time-frequency analysis (TFA) has been identified as a crucial method for examining non-stationary signals; however, many existing techniques face limitations in resolution, particularly in the context of chirp signals, as dictated by the Heisenberg uncertainty principle. To address these challenges, the superlet synchroextracting transform (SLSET) is introduced as an innovative TFA approach that combines the strengths of superlet (SL) and synchroextracting transforms, resulting in improved resolution. This research utilizes NWM alongside SLSET to detect boundary loosening in sandwich beams, demonstrating the method's effectiveness in identifying structural damage while maintaining robustness against noise. Results indicate that SLSET significantly enhances the damage index compared to traditional TFA methods. The high resolution achieved allows for the detection of sidebands in vibro-acoustic modulation (VAM) tests conducted at low pump frequencies. Furthermore, three machine learning (ML) models including support vector machine (SVM), Adaptive Boosting (AdaBoost), and Random Forest (RF) were trained. The stack ensemble method combined the outputs of these models, resulting in an overall accuracy of 99.2%. This approach effectively leveraged the strengths of individual models, enhancing generalization and robustness in detecting damage across complex data scenarios. The features extracted using SLSET for VAM data of faulty structure attains a classification accuracy of 98.9%. In contrast, features derived from conventional time-frequency methods fail to identify damage, even in noise-free conditions.