A Classical Parameter Identification Method and a Modern Test Generation Algorithm

Abstract

Many methods have been presented for the testing and diagnosis of analog circuits. Each of these methods has its advantages and disadvantages. In this paper we propose a novel sensitivity analysis algorithm for the classical parameter identification method and a continuous fault model for the modern test generation algorithm, and we compare the characteristics of these methods. At present, parameter identification based on the component connection model (CCM) cannot ensure that the diagnostic equation is optimal. The sensitivity analysis algorithm proposed in this paper can choose the optimal set of trees to construct an optimal CCM diagnostic equation, and enhance the diagnostic precision. But nowadays increasing attention is being paid to test generation algorithms. Most test generation algorithms use a single value in the fault model. But the single values cannot substitute for the actual faults that may occur, because the possible faulty values vary over a continuous range. To solve this problem, this paper presents a continuous fault model for the test generation algorithm which has a continuous range of parameters. The test generation algorithm with this model can improve the treatment of the tolerance problem, including the tolerances of both normal and faulty parameters, and enhance the fault coverage rate. The two methods can be applied in different situations.

Test Generation Algorithm for Fault Detection of Analog Circuits Based on Extreme Learning Machine

“…There are many researches on fault diagnosis [6–16]. Yang et al propose a fault dictionary method about analog circuits in 2009 [7, 8].…”

“…This method is simple, but it is relatively complicated for establishment of this dictionary. In 2011, Ting proposes an analog circuit fault diagnosis method based on component connection model (CCM) [6], which can properly realize fault location. However, the scale of CCM will become larger with the increase of circuit complexity.…”