Physical Coupling Fusion of Multisensor Data and Multidomain Mixed Features of Electromagnetic Acoustic Sensing System for Stress Measurement

Stress concentrations in ferromagnetic materials, which can arise during manufacturing or in-service use, often compromise structural integrity. Therefore, effective stress monitoring is critical for steel components. Conventional stress analysis methods that rely on single features from individual sensors often lack the required accuracy and robustness. This paper proposes a novel multi-sensor information fusion framework designed to significantly enhance stress prediction accuracy. The framework integrates Magneto-Acoustic Emission (MAE) and Magnetic Barkhausen Noise (MBN) signals, both of which are highly sensitive to stress-induced magnetic microstructure alterations. The proposed methodology involves extracting diverse time-domain and frequency-domain features from MAE and MBN signals acquired under various applied stress conditions. Subsequently, Principal Component Analysis (PCA) is employed for dimensionality reduction of this comprehensive feature set. The reduced features are then input into a developed multimodal Gaussian kernel product fusion model. The parameters of this fusion model are optimized using the Limited-memory Broyden-Fletcher-Goldfarb-Shanno with bounds (L-BFGS-B) algorithm. Experimental results obtained from the specimens demonstrate the superior predictive performance of the proposed Gaussian fusion model, which achieved an R-squared (R2) value of 0.9425 and a Root Mean Square Error (RMSE) of 7.58. This performance represents an improvement over other evaluated fusion models and approaches based on single-sensor data or individual features. This study validates the proposed multi-physics, multi-sensor and multi-feature fusion strategy as a highly reliable and precise method for stress assessment in ferromagnetic materials.