Fault diagnosis of analog circuit based on a second map SVDD

Abstract

To improve the diagnosis accuracy of analog circuit, this paper presents a second map support vector data description (SM-SVDD) method, which uses an anomalous and close surface instead of a hypesphere to describe the target data. The fault classifier is constructed by a SM SVDD algorithm to realize analog circuit fault diagnosis. Experimental results on two typical circuits confirm that the proposed method is effective in analog circuit fault diagnosis with good accuracy, and the performance surpasses other intelligent diagnosis methods, such as back propagation neural network, support vector machine and the normal SVDD. The developed method can be applied to other multi-classifier in electronic applications.

Parametric Fault Diagnosis of Analog Circuits Based on a Semi-Supervised Algorithm

“…The three operational amplifiers active band-stop filter circuit was the same as References [18,42], and was composed of 12 resistors, four capacitors, and three operational amplifiers. Its nominal value is shown in Figure 8, with tolerance ranges of 5% and 10% for resistors and capacitors, respectively.…”

“…In Reference [18], only the single faults were diagnosed. Thus, the single, double, and mixed fault models were taken from Reference [42], and eight common single failures and three combinations ofthem were selected, as shown in Table 5. [18,42],and was composed of 12 resistors, four capacitors, and three operational amplifiers.…”

“…Thus, the single, double, and mixed fault models were taken from Reference [42], and eight common single failures and three combinations ofthem were selected, as shown in Table 5. [18,42],and was composed of 12 resistors, four capacitors, and three operational amplifiers. Its nominal value is shown in Figure 8, with tolerance ranges of 5% and 10% for resistors and capacitors, respectively.…”

“…In Reference [18], only the single faults were diagnosed. Thus, the single, double, and mixed fault models were taken from Reference [42], and eight common single failures and three combinations ofthem were selected, as shown in Table 5. For a certain fault range, 100 fault values were randomly selected, components of which were analyzed using the Monte-Carlo method to generate 100 combinations; the instance value was 5% of other resistances and 10% of other capacitances, and the distribution was Gaussian.…”

Subspace Support Vector Data Description

“…In kernel issue extensions, the main focus is on reducing the complexity or proposing new kernels for one-class classification. For example, in [42] and [43], new kernels are proposed to improve the accuracy of SVDD. Proposing changes in the boundary for enclosing the target data comes under third category for improving one-class classification accuracy.…”