Damage Detection and Localization in a Cantilever Beam Structure Via Regression-and-Classification-Based Machine Learning

Recent advancements in structural health monitoring have been significantly driven by the integration of artificial intelligence technologies. This study employs a combination of supervised machine learning techniques, including classification and regression, to accurately detect and localize local thickness reduction defects in a cantilever beams. Our approach utilizes a dataset of 100 signals, comprising 84 defective and 16 healthy states of the beam’s free side displacement, for training machine learning models. Signal processing involves the application of five distinct mode decomposition methods to decompose each signal into its Intrinsic Mode Functions (IMFs). Additionally, four dimensionality reduction methods have been used to reduce the dimensions of the signals. Feature extraction is performed using seven frequency domain, two time domain, and three time–frequency domain methods to capture pertinent patterns and characteristics within the signals. We evaluate the performance of five classification methods and 10 regression methods to predict the location of defects. Our results demonstrate the efficacy of combining specific feature extraction and dimensionality reduction techniques with classification methods, achieving multi-class classification accuracies of up to 99.55%. Moreover, regression methods, particularly the Bayesian ridge regressor, exhibit high accuracy in predicting defect locations, with an $R^2$ value of 99.94% and minimal Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) values. This study highlights the potential of integrating regression and classification-based machine learning approaches for precise damage detection and localization in beam structures.