Czasopismo | ELEKTRONIKA - KONSTRUKCJE, TECHNOLOGIE, ZASTOSOWANIA | Rocznik 2013 - zeszyt 2

Test points selection algorithms improving efficiency of analog circuit fault diagnosis

nr katalogowy: 74678

While considering modern modeling, designing and manufacturing process, fault diagnosis of analog circuits with element tolerances must be taken into account. It has always been an important part of this process. There exist two main methods for taking into account parameters variations. First one is direct and based on the tolerance regions calculations [1]. The second one, indirect, is grounded on the use of specified classifiers including tolerance in the learning stage of pattern recognition process. Deng Julong introduced algorithms using some ideas of grey relational data analysis. They have various applications in economics, industry and agriculture, and most recently have been also introduced to the analog circuit fault diagnosis [2--5]. With the indirect methods mentioned above there is one problem - they require very large data sets. There, however, seem to be two important methods for dealing with this - data mining technique called feature selection [6] and use of the classification algorithms having the natural property of dealing with uncompleted information. This article refers to test points selection for fault-driven analysis of analog circuits based on some grey systems properties. Simple fault diagnostic strategy for analog circuits with tolerance included is presented, illustrated with the examples and discussed. Algorithms enabling to detect and localize single and multiple catastrophic faults, are prepared under assumption that input data sets are extracted from DC analysis. Basis of the ranking list algorithm To perform final classification procedure we prepare data set followed from measurements and preprocessing stage: (1) where N is a number of features defined for the circuit under test, M - number of all investigated states of the circuit under test (CUT) including nominal one. Relational degree of the CUT is defined as (2) where (3) denotes the vector of weight coefficients and γ jk is re[...]

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2013-2

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