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

作者：Zhang, Zhen^*; Duan, Zhemin; Long, Ying; Yuan, Lifen

来源：Analog Integrated Circuits and Signal Processing, 2014, 81(1): 289-297.

DOI：10.1007/s10470-014-0373-2

• Switched current circuit
• Fault diagnosis
• Entropy
• Kurtosis
• Support vector machine (SVM)
• Particle swarm optimization (PSO)

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 low-pass 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.

• 出版日期2014-10
• 单位湖南大学; 西北工业大学