In-Vivo Fault Analysis and Real-Time Fault Prediction for RF Generators Using State-of-the-Art Classifiers

Author

Chandrashekar, Girish ; Sahin, Ferat ; Cinar, E. ; Radomski, Aaron ; Sarosky, Dan

Author_Institution

Electr. & Microelectron. Eng., Rochester Inst. of Technol., Rochester, NY, USA

fYear

2013

fDate

13-16 Oct. 2013

Firstpage

1634

Lastpage

1639

Abstract

In this paper we apply various machine learning techniques for fault detection of RF (Radio Frequency) Power Generators. Fast Fourier Transform features are used in our analysis for all experiments. Radial Basis Function Networks (RBF) is used to build a two class classifier to differentiate between normal and one fault condition. We apply three one class classifiers to model the normal operating conditions. The data is obtained from five different generators of the same model type.

Keywords

fast Fourier transforms; fault diagnosis; learning (artificial intelligence); power engineering computing; signal generators; RBF networks; RF generators; fast Fourier transform features; fault analysis; fault condition; fault detection; machine learning techniques; one class classifiers; radial basis function networks; radio frequency power generators; real-time fault prediction; state-of-the-art classifiers; two class classifier; Data models; Fault detection; Feature extraction; Generators; Mathematical model; Radio frequency; Training; Fault analysis; Novelty detection; One class classification; RF generators; Radial Basis Functions;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics (SMC), 2013 IEEE International Conference on

Conference_Location

Manchester

Type

conf

DOI

10.1109/SMC.2013.282

Filename

6722035