Reliability Fatigue Assessment Based on Electromagnetic Features and Stacking Ensemble Learning

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Author(s): Chen Wu (Suzhou Polytechnic University, China), Ye Shi (State Grid Anhui Susong Electric Power Company, China)and Feng Bu (Suzhou Polytechnic University, China)
Copyright: 2026
Volume: 22
Issue: 1
Pages: 14
Source title: International Journal on Semantic Web and Information Systems (IJSWIS)
Editor(s)-in-Chief: Brij Gupta (Asia University, Taichung City, Taiwan)
DOI: 10.4018/IJSWIS.401495

Keywords: Computer Science & IT / Engineering Science Reference / Semantic Web / Web Technologies

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Abstract

Fatigue failure is a common mode of failure in ferromagnetic materials, yet traditional electromagnetic detection techniques and assessment models struggle to accurately estimate fatigue levels. To address this, a reliability method for evaluating the fatigue degree of ferromagnetic materials is proposed in this paper, integrating multi-electromagnetic features with a multi-model stacking ensemble learning approach. Four types of electromagnetic parameters, namely magnetic Barkhausen noise, incremental permeability, tangential magnetic field, and hysteresis loops, are collected as feature parameters. Subsequently, a stacking ensemble learning framework is constructed. Least squares support vector machine models serve as base learners with a multivariate linear regression model serving as the meta-learner. Experimental results on Q345 steel samples demonstrate that the proposed method achieves an average error rate of 7.3% in fatigue degree assessment, enabling early fatigue detection and lifespan prediction for materials.

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Reliability Fatigue Assessment Based on Electromagnetic Features and Stacking Ensemble Learning

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