Аңдатпа
Structural health monitoring systems generate large volumes of multichannel dynamic data under external impacts, which require efficient processing methods for timely damage detection. This study aimed to develop and evaluate a parallel approach for processing structural monitoring data and diagnosing structural conditions using machine learning techniques. The proposed methodology included signal preprocessing, segmentation, normalization, and extraction of statistical, energy, and spectral features from multichannel vibration and deformation signals. A Random Forest classifier was trained to distinguish between the undamaged and damaged structural states. The model was implemented in Python using signal processing and machine learning libraries, while parallel data processing was applied at the levels of channel analysis, time-window processing, and feature extraction to reduce computational complexity. The experimental results demonstrated high classification performance. The developed model achieved a hold- out ROC–AUC of 1.000, indicating strong separability between the structural states. Five-fold cross-validation further confirmed the robustness of the proposed approach. Feature importance analysis showed that spectral characteristics, particularly bandwidth-related features, made the greatest contribution to classification accuracy. The obtained results also demonstrated that parallel processing significantly improved computational efficiency when analyzing large multichannel datasets. The study confirmed that the integration of signal processing, machine learning, and parallel computing provided an effective framework for structural condition diagnostics under dynamic loading. The proposed approach can be applied in structural health monitoring systems for early damage detection and near real-time analysis of monitoring data. Future research will focus on expanding the dataset and improving the generalization capability of the model.