Intelligent Fault Detection and Identification System for Analog Electronic Circuits Based on Fuzzy Logic Classifier

Ahmed R. Nasser*, Ahmed Taher Azar, AMMAR AL MHDAWI, Amjad J. Humaidi, Ibraheem K. Ibraheem

*Corresponding author for this work

Research output: Contribution to journalArticle (journal)peer-review

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Abstract

Analog electronic circuits play an essential role in many industrial applications and control systems. The traditional way of diagnosing failures in such circuits can be an inaccurate and time-consuming process; therefore, it can affect the industrial outcome negatively. In this paper, an intelligent fault diagnosis and identification approach for analog electronic circuits is proposed and investigated. The proposed method relies on a simple statistical analysis approach of the frequency
response of the analog circuit and a simple rule-based fuzzy logic classification model to detect and identify the faulty component in the circuit. The proposed approach is tested and evaluated using a commonly used low-pass filter circuit. The test result of the presented approach shows that it can identify the fault and detect the faulty component in the circuit with an average of 98% F-score
accuracy. The proposed approach shows comparable performance to more intricate related works.
Original languageEnglish
Article number10232888
JournalElectronics (Switzerland)
Volume10
Issue number23
Early online date23 Nov 2021
DOIs
Publication statusE-pub ahead of print - 23 Nov 2021

Keywords

  • artificial intelligence
  • analog electronic circuits
  • fault diagnosis and identification

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