Insulation failures prognosis in electric machines: preventive detection and time to failure forecast | Zendy

Guedes Armando S. | Zendy; Silva Sidelmo Magalhaes | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Insulation failures prognosis in electric machines: preventive detection and time to failure forecast

Author(s) -

Guedes Armando S.,

Silva Sidelmo Magalhaes

Publication year - 2020

Publication title -

iet electric power applications

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.815

H-Index - 97

eISSN - 1751-8679

pISSN - 1751-8660

DOI - 10.1049/iet-epa.2019.0711

Subject(s) - preventive maintenance , reliability engineering , engineering , computer science , forensic engineering

This work presents a new approach to the on‐line evaluation of the insulation of electric machines. Through the proposed system, it is possible to identify the stress agents causing the degradation of the insulation of low‐ and medium‐voltage machines. In addition, the estimation of the time‐to‐failure (TF) of the insulation is developed based on linear stochastic models autoregressive moving average and artificial neural networks. The identification of the stress agent and the estimation of the TF give a complete prognosis for the predictive monitoring of the machine insulation.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research