Physical/mathematical laws describing electrical insulation aging play a key role for the reliability model identification of the insulation itself. This holds for the popular Inverse Power Model, too. The paper first discusses the deduction of the Inverse Power Model from reasonable physical and mathematical models of ageing, described via proper characterization of the random variables or the stochastic processes involved. Then, some analytical aids are given in order to perform its identification and Bayes Estimation, also by means of numerical applications with reference to in-service electrical failure data.
Genesis, Identification and Bayes Estimation of the Inverse Power Model for Insulation Reliability Assessment / Chiodo, E.; Di Noia, L. P.; Mottola, F.; Mazzanti, G.. - (2018), pp. 370-373. (Intervento presentato al convegno 2018 IEEE CEIDP Conference on Electrical Insulation and Dielectric Phenomena, CEIDP 2018 tenutosi a Iberostar Hotel and Resort Cancun, Boulevard Kukulcan Km. 17, mex nel 2018) [10.1109/CEIDP.2018.8544863].
Genesis, Identification and Bayes Estimation of the Inverse Power Model for Insulation Reliability Assessment
Chiodo, E.;Di Noia, L. P.;Mottola, F.;
2018
Abstract
Physical/mathematical laws describing electrical insulation aging play a key role for the reliability model identification of the insulation itself. This holds for the popular Inverse Power Model, too. The paper first discusses the deduction of the Inverse Power Model from reasonable physical and mathematical models of ageing, described via proper characterization of the random variables or the stochastic processes involved. Then, some analytical aids are given in order to perform its identification and Bayes Estimation, also by means of numerical applications with reference to in-service electrical failure data.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
