Fault diagnosis of analog circuits using systematic tests based on data fusion

Minfang Peng, Chi Kong Tse, Meie Shen, Kai Xie

Research output: Journal article publicationJournal articleAcademic researchpeer-review

14 Citations (Scopus)

Abstract

An analog fault diagnosis approach using a systematic step-by-step test is proposed for fault detection and location in analog circuits with component tolerance and limited accessible nodes. First, by considering soft faults and component tolerance, statistics-based fault detection criteria are established to determine whether a circuit is faulty by measuring accessible node voltages. For a faulty circuit, fuzzy fault verification is performed using the accessible node voltages. Furthermore, using an approximation technique, the most likely faulty elements are identified with a limited number of circuit gain measurements at selected frequencies. Finally, employing the D-S evidence theory, synthetic decision is made to locate faults according to the results of fault verification and estimation. Unlike other methods which use a single diagnosis method or a particular type of measurement information, the proposed approach makes use of the redundancy of different types of measurement information and the combined use of different diagnosis methods so as to improve diagnosis accuracy.
Original languageEnglish
Pages (from-to)525-539
Number of pages15
JournalCircuits, Systems, and Signal Processing
Volume32
Issue number2
DOIs
Publication statusPublished - 1 Apr 2013

Keywords

  • Analog circuit
  • Data fusion
  • Fault detection
  • Fault estimation
  • Fault verification

ASJC Scopus subject areas

  • Signal Processing
  • Applied Mathematics

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