An Improved Electromagnetism-like Algorithm for Gaussian Process Hyperparameter Optimization in Remaining Useful Life Prediction of Electrolytic Capacitors

This paper introduces an innovative optimization procedure to enhance gaussian process regression (GPR) for hyperparameter tuning. The technique estimates the remaining useful life (RUL) of electrolytic capacitors under electrical overstress conditions. The traditional electromagnetism-like (EM) optimization technique suffers premature convergence and insufficient exploitation near local optimal solutions. It also suffers ineffective charge relocation within high-dimensional search spaces. To address these limitations, the adaptive chaotic opposition-based learning electromagnetism-like (ACOEM) algorithm is proposed. The new algorithm incorporates three techniques to overcome these challenges: opposition-based learning to increase initial population size and speed up the optimization process; chaotic map-based scaling of forces to strike a balance between exploration and exploitation; and lévy flights to avoid local minima. The ACOEM optimizer fine-tunes the hyperparameters of the GPR kernel by minimizing the negative log marginal likelihood (NLML) objective function. The proposed ACOEM-GPR predictor is successfully applied to National Aeronautics and Space Administration (NASA’s) electrochemical capacitor electrical overstress dataset based on three (3) stress levels: 10V, 12V and 14V. Compared to the vanilla GPR predictor, ACOEM-GPR exhibited superior performance in terms of the correlation coefficient , improving from 0.3015 to 0.7482 for 10V stress and from 0.5615 to 0.8921 for 14V stress. Additionally, the ACOEM-GPR method showed significant improvements in mean squared error (MSE) reducing the MSE from 9458 to 3410 and root mean squared error (RMSE) from 97.3 to 58.4 at 10V. ACOEM-GPR is proven to produce accurate and robust RUL predictions allowing for effective predictive maintenance of power converter capacitors.