Fault Diagnosis of Analog Circuits Using Systematic Tests Based on Data Fusion
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Fault Diagnosis of Analog Circuits Using Systematic Tests Based on Data Fusion Minfang Peng · Chi K. Tse · Meie Shen · Kai Xie
Received: 10 January 2012 / Revised: 28 August 2012 / Published online: 19 September 2012 © Springer Science+Business Media, LLC 2012
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.
M. Peng College of Electrical and Information Engineering, Hunan University, Changsha, 410082, China e-mail: [email protected] C.K. Tse () Department of Electronic and Information Engineering, Hong Kong Polytechnic University, Hong Kong, China e-mail: [email protected] M. Shen College of Computer Science, Beijing University of Information Science and Technology, Beijing, 100101, China e-mail: [email protected] K. Xie Electronics and Information School, Yangtze University, Hubei, 434023, China e-mail: [email protected]
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Circuits Syst Signal Process (2013) 32:525–539
Keywords Analog circuit · Fault detection · Fault verification · Fault estimation · Data fusion
1 Introduction Fault diagnosis of analog circuits is a very important problem in the design and testing of electronic circuits. It has been reported that although above 80 % of circuit implementations are digital, over 80 % of the faults occur in analog circuits [15]. This overwhelming portion of faults happening in analog circuits has prompted intensive research on fault diagnosis for analog circuits [1–5, 7–12, 14, 15, 17–36]. In general, fault diagnosis includes detecting faulty circuits, locating or identifying faulty components, and determining their parameter ranges where faults occur. In recent years, attention has been directed to fault detection and location, especially in circuits with component tolerance [1–4, 7–12, 14, 15, 17–34]. A number of approaches have been proposed to address this problem based on the use of intelligent information processing technologies including neural networks [3, 4, 7, 22, 33, 34], wavelet transform [4, 27] and fuzzy theory [7, 23, 30]. Statistical methods [14, 17–20]
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