Hygrothermal Degradation of Epoxy Electrical Insulating Material—Testing and Mathematical Modeling
Author:
Leffler Jan1ORCID, Kaska Jan2ORCID, Kadlec Petr1ORCID, Prosr Pavel1ORCID, Smidl Vaclav3ORCID, Trnka Pavel1ORCID
Affiliation:
1. Department of Materials and Technology, Faculty of Electrical Engineering, University of West Bohemia, 301 00 Pilsen, Czech Republic 2. Department of Electrical and Computational Engineering, Faculty of Electrical Engineering, University of West Bohemia, 301 00 Pilsen, Czech Republic 3. Research and Innovation Centre for Electrical Engineering, Faculty of Electrical Engineering, University of West Bohemia, 301 00 Pilsen, Czech Republic
Abstract
The degradation of electrical insulating materials has been a subject of interest for decades as they are commonly applied in many fields of electrical engineering. Suitably modeling such a process is important since the known and well-described degradation process reveals the effect of ambient conditions, and this allows us to possibly estimate a material’s remaining useful life. However, not many studies are dealing with the effect of the hygrothermal degradation of impregnating mono-component epoxy resins in the context of electrical engineering. Therefore, this study deals with this issue and discusses both the dielectric response (based on the measurement of relative permittivity, dissipation factor, and dielectric strength) and the mechanical response (based on measurements of tensile strength and Shore D hardness) to a hygrothermal degradation experiment. In addition, the results of thermal analyses are presented for the evaluation of the pristine specimen manufacturing process and possible post-curing processes. Furthermore, this study presents several methodologies for modeling the degradation process, including a novel methodology in this area based on Bayesian experimental design. As an outcome, mechanical parameters are proven to be specific in terms of the actual condition of the material and the Bayesian enhanced degradation model seems to be superior to the conventional evaluation methods in this particular study.
Funder
project Bayesian Experimental Design for the Development of Material-Aging Models project Materials and Technologies for Electrical Engineering project Computational Intelligence-Assisted Design of Electric and Electronic Devices
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