Analog circuit fault diagnosis based on GMKL-SVM method

作者：Zhang, Chaolong; He, Yigang; Yuan, Lifen; Li, Zhigang; Xiang, Sheng

来源：Chinese Journal of Scientific Instrument, 2016, 37(9): 1989-1995.

• Analog circuit fault diagnosis
• Classification models
• Haar wavelet transform
• Multiple Kernel Learning
• Quantum-behaved particle swarm optimization
• Quantum-behaved particle swarm optimization(QPSO) algorithm
• Wavelet coefficients
• Wavelet features

A novel analog circuit fault diagnosis approach is presented by using generalized multiple kernel learning- support vector machine (GMKL-SVM) algorithm. Firstly, the wavelet coefficients of measured time responses are generated as features by using a Haar wavelet transform. Then, wavelet features are used as samples to identify parameters for GMKL-SVM method with using quantum-behaved particle swarm optimization (QPSO) algorithm. As a result, classification model based on GMKL-SVM method is constructed to diagnosis analog circuit faults. Dignostics on both single fault and double faults demonstrate that the proposed GMKL-SVM method can obtain good diagostic performance on analog circuit fault diagnosis. Additionally, compared to the traditional GMKL-SVM method, the presented approach has higher diagnostic precision.

• 出版日期2016
• 单位合肥工业大学