Early Detection of SMPS Electromagnetic Interference Failures Using Fuzzy Multi-Task Functional Fusion Prediction
Volume 9, Issue 4, Page No 35-50, 2024
Author’s Name: Declan Mallamo*, Michael Azarian, Michael Pecht
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Center for Advanced Life Cycle Engineering, University of Maryland, College Park, MD 20742, USA
a)whom correspondence should be addressed. E-mail: dmallamo@umd.edu
Adv. Sci. Technol. Eng. Syst. J. 9(4), 35-50 (2024); DOI: 10.25046/aj090405
Keywords: Prognostics Health Management, Functional Data Analysis, Electromagnetic Interference, Multitask Lasso Regression, Aluminum Electrolytic Capacitors
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This study addresses the need for improved prognostics in switch-mode power supplies (SMPS) that incorporate electromagnetic interference (EMI) filters, with a focus on aluminum electrolytic capacitors, which are critical for the reliability of these systems. The primary aim is to develop a robust model-based approach that can accurately predict the degradation and operational lifetime of these capacitors under varying environmental conditions. To achieve this, the research employs a generalized state space averaging technique to simulate a population of impending degradation trajectories for the capacitors. Environmental and degradation effects are modeled comprehensively. Frequency-based test features are derived from the gain, control, and impedance transfer functions of the filter and SMPS. These features are fitted with b-spline functionals for resampling and subsequently analyzed using functional principal component analysis to project the data onto the principal modes of variation. The extracted features serve as inputs to a fuzzy multi-task functional fusion predictor, which estimates the state of health at critical frequencies. The effectiveness of this model-based approach is validated through extensive experimentation, demonstrating its potential to significantly enhance the predictive maintenance strategies for SMPS with EMI filters.
Received: 01 May 2024, Revised: 04 July 2024, Accepted: 05 July 2024, Published Online: 26 July 2024
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