A new swarm-SVM-based fault diagnosis approach for switched current circuit by using kurtosis and entropy as a preprocessor

Abstract

This paper presents a new fault diagnosis method for switched current (SI) circuits. The kurtoses and entropies of the signals are calculated by extracting the original signals from the output terminals of the circuit. Support vector machine (SVM) is introduced for fault diagnosis using the entropies and kurtoses as inputs. In this technique, a particle swarm optimization is proposed to optimize the SVM to diagnose switched current circuits. The proposed method can identify faulty components in switched current circuit. A lowpass SI filter circuit has been used as test beached to verify the effectiveness of the proposed method. The accuracy of fault recognition achieved is about 97 % although there are some overlapping data when tolerance is considered. A comparison of our work with Long et al. (Analog Integr Circuit Signal Process 66:93-102, 2011), which only used entropy as a preprocessor, reveals that our method performs well in the part of fault diagnostic accuracy.

Analog Circuit Fault Diagnosis via Joint Cross-Wavelet Singular Entropy and Parametric t-SNE

“…Many scholars have devoted themselves to the field of feature extraction with use of entropy techniques. Approximative maximum entropy (Apen) has been used to diagnosis faults [9,21]. However, a bad performance could be obtained when processing the short data-set.…”

Fault Diagnosis of Analog Filter Circuit Based on Genetic Algorithm

“…It proposed a new decision tree approach for analog circuit fault diagnosis using binary support vector machines (BSVMs) that are trained by using different data sets in [10]. The kurtoses and entropies of the output signals are calculated, which is treated as inputs to train SVM used to fault recognition [11]. Zhang et al [12] applied generalized multiple kernel learning-support vector machine (GMKL-SVM) method and particle swarm optimization (PSO) algorithm to diagnose fault.…”

Fault diagnosis of analog circuit based on a second map SVDD

“…Intelligent methods such as neural network (NN), statistical learning theory (SLT) are widely used in fault diagnosis [3][4][5][6][7][8][9]. Various NN methods are employed to improve the diagnosis accuracy and efficiency [4][5][6][7].…”

“…SLT proposed by Vapnik can solve problems such as small samples. Support vector machine (SVM) developed from SLT, has been successfully applied in fault diagnosis [9,10]. SVM has higher generalization ability, which can be used to solve problems such as small samples, nonlinearity, over fitting, high dimension and local minima [9][10][11].…”