Automated diagnostics of analog systems using fuzzy logic approach

被引：26

作者：

Bilski, Piotr ^{[1
]}

Wojciechowski, Jacek M. ^{[2
]}

机构：

[1] Agr Univ Warsaw, Dept Informat, PL-02776 Warsaw, Poland

[2] Warsaw Univ Technol, Inst Radioelect, Dept Radiocommun, PL-00665 Warsaw, Poland

来源：

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT | 2007年 / 56卷 / 06期

关键词：

analog systems; artificial intelligence; diagnostics; machine learning;

D O I：

10.1109/TIM.2007.908152

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper presents an automated method for analog system diagnostics, which aims to detect and localize multiple faults in noisy conditions. The generic architecture of the diagnostic scheme and its stages of denoising, stamp extraction, and fault detection are explained. The method is tested on three systems of various physical nature. Then, approaches to automated diagnostics of the different classes of the systems are proposed. Machine learning methods (decision-tree-based fuzzy logic) are used to effectively detect faults. Their advantages are explained and confirmed by examples.

引用

页码：2175 / 2185

页数：11

共 23 条

[1] Wavelet and subband transforms: Fundamentals and communication applications [J].