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Jagdeep Singh. Foto.

Jagdeep Singh

Forskare

Jagdeep Singh. Foto.

An intelligent model for residual life prediction of thyristor

Författare

  • Cherry Bhargava
  • Jagdeep Singh
  • Pardeep Kumar Sharma

Summary, in English

Modern age is the age of integration, where millions of electronic components are integrated and installed on a single chip, to minimize the size of device and automatically increases the speed. But, as a greater number of components are placed on a single device, reliability becomes a concern issue, as failure of one component can degrade the complete device. From dimmer to high voltage power transmission, thyristors are widely used. The failure of thyristor can be proven dangerous for mankind, so the reliability prediction of thyristor is highly desirable. This paper is based on the accelerated life testing based experimental technique for reliability assessment. An intelligent model is designed using artificial intelligence techniques i.e. ANN, Fuzzy and ANFIS and comparative analysis is conducted to estimate the most accurate technique. Fuzzy based Graphical User Interface (GUI) is framed which informs the user about the live status of thyristor under various environmental conditions. The intelligent techniques are validated using experimental technique. An error analysis is conducted to predict the most accurate and reliable system for residual life prediction of thyristor. Out of all prediction techniques, ANFIS has the highest accuracy i.e. 95.3%, whereas ANN and Fuzzy inference system has accuracy range 86.1% and 89.2% respectively.

Avdelning/ar

  • Internationella miljöinstitutet

Publiceringsår

2019-06-01

Språk

Engelska

Sidor

1862-1866

Publikation/Tidskrift/Serie

International Journal of Engineering and Advanced Technology

Volym

8

Issue

5

Dokumenttyp

Artikel i tidskrift

Förlag

Blue Eyes Intelligence Engineering and Sciences Publication

Ämne

  • Other Electrical Engineering, Electronic Engineering, Information Engineering

Nyckelord

  • Accelerated life testing
  • Artificial intelligence
  • Graphical user interface
  • Reliability prediction
  • Thyristor

Aktiv

Published

ISBN/ISSN/Övrigt

  • ISSN: 2249-8958