PSO优化多核RVM的模拟电路故障预测
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摘要
针对模拟电路健康管理的特点,提出了一种基于PSO优化多核RVM的模拟电路故障预测方法。利用参数分析得到电路的输出频域响应作为特征,计算其与电路无故障标准响应的欧氏距离来表征电路元件健康值,将多个核函数线性组合,并用PSO优化多核RVM参数后的模型实现对各个时间点元件的健康值变化轨迹进行预测。仿真结果表明,该方法在小样本情况下,预测效果优于单一核函数的RVM模型,适用于健康管理中实时预测,具有较好的实用性。
Aimed at the characteristics of prognostic and health management in analog circuits, a new method for analog circuit fault prediction based on particle swarm optimized multi-kernel RVM is proposed in this paper. Firstly, fault features are extracted by parameter analysis of the circuit, then the health value of circuit components is represented through calculating the Euclidean distance. Finally, combining different kernel function linearly, the trend of health value trajectory with respect to time points can be predicted by particle swarm optimization, the proposed approach is appropriate for real time prediction in prognostic and health management and is better than the single kernel RVM model in the case of small sample. Simulation results validate the good practicability and effectiveness of the proposed method.
Aimed at the characteristics of prognostic and health management in analog circuits, a new method for analog circuit fault prediction based on particle swarm optimized multi-kernel RVM is proposed in this paper. Firstly, fault features are extracted by parameter analysis of the circuit, then the health value of circuit components is represented through calculating the Euclidean distance. Finally, combining different kernel function linearly, the trend of health value trajectory with respect to time points can be predicted by particle swarm optimization, the proposed approach is appropriate for real time prediction in prognostic and health management and is better than the single kernel RVM model in the case of small sample. Simulation results validate the good practicability and effectiveness of the proposed method.
引文
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[4]Yu W X,Sui Y,Wang J.The faults diagnostic analysis for analog circuit based on FA-TM-ELM[J].Journal of Electronic Testing,2016,32(4):1-7.
[5]胡鸿志,田书林,郭庆,等.面向三维空间的模拟电路故障建模与故障检测[J].仪表技术与传感器,2015(10):101-103.
[6]周靖宇,田书林,王厚军,等.采用电压特征的模拟电路单元件预测方法研究[J].微电子学与计算机,2015,32(12):44-48.
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[11]邴其春,龚勃文,杨兆升,等.一种组合核相关向量机的短时交通流局域预测方法[J].哈尔滨工业大学学报,2017,49(3):144-149.
[12]张朝龙,何怡刚,邓芳明,等.一种基于QPSO-RVM的模拟电路故障预测方法[J].仪器仪表学报,2014,35(8):1751-1757.
[13]刘张亮,邓芳明,郝勇,等.基于余弦距离和超限学习机的模拟电路故障预测方法[J].仪表技术与传感器,2016(8):84-86.
[14]祁涛,张彦斌,邸凯,等.PSO优化LS-SVM在模拟电路故障预测中的应用[J].计算机测量与控制,2016,24(3):4-7.
[15]Tipping M E.The relevance vector machine[C]//Proceedings of Conference on Neural Information Processing Systems.[S.l.]:MIT Press,2000:652-658.
[16]Tipping M E.Sparse Bayesian learning and the relevance vector machine[J].Journal of Machine Learning Research,2001,3(1):211-244.
[17]Tipping M E.Bayesian inference:an introduction to principles and practice in machine learning[C]//Proceedings of Conference on Machine Learning.[S.l.]:Springer,2004:41-62.
[2]Eddahech A,Briat O,Bertrand N,et al.Behavior and stateof-health monitoring of Li-ion batteries using impedance spectroscopy and recurrent neural networks[J].International Journal of Electrical Power&Energy Systems,2012,42(1):487-494.
[3]Wang S,Zhao L,Su X,et al.Prognostics of Lithium-Ion batteries based on battery performance analysis and flexible support vector regression[J].Energies,2014,7(10):6492-6508.
[4]Yu W X,Sui Y,Wang J.The faults diagnostic analysis for analog circuit based on FA-TM-ELM[J].Journal of Electronic Testing,2016,32(4):1-7.
[5]胡鸿志,田书林,郭庆,等.面向三维空间的模拟电路故障建模与故障检测[J].仪表技术与传感器,2015(10):101-103.
[6]周靖宇,田书林,王厚军,等.采用电压特征的模拟电路单元件预测方法研究[J].微电子学与计算机,2015,32(12):44-48.
[7]Vasan A S S,Long B,Pecht M.Diagnostics and prognostics method for analog electronic circuits[J].IEEE Transactions on Industrial Electronics,2013,60(11):5277-5291.
[8]段青,赵建国,马艳.优化组合核函数相关向量机电力负荷预测模型[J].电机与控制学报,2010,14(6):33-38.
[9]雷亚国,陈吴,李乃鹏,等.自适应多核组合相关向量机预测方法及其在机械设备剩余寿命预测中的应用[J].机械工程学报,2016,52(1):87-93.
[10]张洋,陈浩,郭波,等.锂离子电池剩余寿命在线预测[J].机械科学与技术,2016,35(8):1286-1290.
[11]邴其春,龚勃文,杨兆升,等.一种组合核相关向量机的短时交通流局域预测方法[J].哈尔滨工业大学学报,2017,49(3):144-149.
[12]张朝龙,何怡刚,邓芳明,等.一种基于QPSO-RVM的模拟电路故障预测方法[J].仪器仪表学报,2014,35(8):1751-1757.
[13]刘张亮,邓芳明,郝勇,等.基于余弦距离和超限学习机的模拟电路故障预测方法[J].仪表技术与传感器,2016(8):84-86.
[14]祁涛,张彦斌,邸凯,等.PSO优化LS-SVM在模拟电路故障预测中的应用[J].计算机测量与控制,2016,24(3):4-7.
[15]Tipping M E.The relevance vector machine[C]//Proceedings of Conference on Neural Information Processing Systems.[S.l.]:MIT Press,2000:652-658.
[16]Tipping M E.Sparse Bayesian learning and the relevance vector machine[J].Journal of Machine Learning Research,2001,3(1):211-244.
[17]Tipping M E.Bayesian inference:an introduction to principles and practice in machine learning[C]//Proceedings of Conference on Machine Learning.[S.l.]:Springer,2004:41-62.