Fault Diagnosis of Analog Circuits Using Systematic Tests Based on Data Fusion
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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.
KeywordsAnalog circuit Fault detection Fault verification Fault estimation Data fusion
This work was supported by National Natural Science Foundation of China under Grants 61173108, 60973032 and 60673084, and Hunan Provincial Natural Science Foundation of China under Grants 06JJ4075 and 10JJ2045.
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