基于多维标度的非线性模拟电路故障诊断方法
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摘要
在非线性模拟电路故障诊断中,考虑到在电路的不同故障状态下,电路的输出响应也会各不相同。针对这种输出响应的差异性,提出了一种基于多维标度(MDS)的非线性模拟电路故障诊断的方法,将电路不同故障状态下输出响应的差异性,转换为一个低维的拟合构图,从而分辨不同的故障类型。仿真结果表明:在电路的单故障情况下,提出的方法能有效地将不同的故障进行分类,实现故障元件的定位,并在低维坐标系中直观地观察不同故障间的差异性,具有较高的准确率。
In nonlinear analog circuit fault diagnosis,considering different fault state of circuit,the output response of the circuit varies. A method for fault diagnosis of nonlinear analog circuits on the basis of multidimensional scaling( MDS) is put forward,aiming at variable output response. This method can transform the difference of output response under different fault states into a low-dimensional fitting composition,so as to distinguish different fault types. Simulation result proves that under the condition of single fault of the circuit,the method has higher accuracy,can effectively classifying the different fault types,locate the faulty components,and observe the difference among variable faults intuitively in low-dimensional coordinate system.
In nonlinear analog circuit fault diagnosis,considering different fault state of circuit,the output response of the circuit varies. A method for fault diagnosis of nonlinear analog circuits on the basis of multidimensional scaling( MDS) is put forward,aiming at variable output response. This method can transform the difference of output response under different fault states into a low-dimensional fitting composition,so as to distinguish different fault types. Simulation result proves that under the condition of single fault of the circuit,the method has higher accuracy,can effectively classifying the different fault types,locate the faulty components,and observe the difference among variable faults intuitively in low-dimensional coordinate system.
引文
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[11]杨本娟,黎小平.基于核主成分分析的图像模糊篡改检测算法[J].传感器与微系统,2015,34(11):137-139.
[12]王承,陈光踽,谢永乐.基于主元分析与神经网络的模拟电路故障诊断[J].电子测量与仪器学报,2005,19(5):14-17.
[13] Borg I,Groener P. Modern multidimensional scaling:Theory and applications[M]. New York:Springer-Verlag,1997.
[2] Baumann R. Soft errors in advanced computer systems[J]. IEEE Transactions on Design&Test of Computers,2005,22(3):258-266.
[3]张伟,许爱强,陈振林.模拟电路节点电压灵敏度权序列故障字典法[J].电子测量与仪器学报,2006,20(4):46-49.
[4]董辰皓,林争辉,秦建业.模拟电路故障诊断的辨识及其方程[J].上海交通大学学报,1995,29(1):138-143.
[5]师宇杰,张耀升.模拟电路故障诊断的VM验证算法[J].天津大学学报,1998,31(5):600-605.
[6]王春香,李丽宏,张帝.基于小波变换和DBN的汽车衡传感器故障诊断[J].传感器与微系统,2016,35(4):22-24.
[7] Aminian M,Aminian F. Neural network based analog circuit fault diagnosis using wavelet transform as preprocessor[J]. IEEE Trans on Circuits Syst,2000,47(2):151-156.
[8]谢保川,刘福太.支持向量机在模拟电路故障诊断中的应用[J].计算机仿真,2006,23(4):39-41.
[9] Zhang A H,Chen C. Fault diagnosis-based semi-supervised global LSSVM for analog circuit[C]∥International Conference on Mechatronics and Control,2015:744-748.
[10] Maiden Y,Jervis B W,Fouillat P,et al. Using artificial neural networks or lagrange interpolation to characterize the faults in an analog circuit:An experimental study[J]. IEEE Trans on Instrumentation and Measurement,1999,48(5):932-938.
[11]杨本娟,黎小平.基于核主成分分析的图像模糊篡改检测算法[J].传感器与微系统,2015,34(11):137-139.
[12]王承,陈光踽,谢永乐.基于主元分析与神经网络的模拟电路故障诊断[J].电子测量与仪器学报,2005,19(5):14-17.
[13] Borg I,Groener P. Modern multidimensional scaling:Theory and applications[M]. New York:Springer-Verlag,1997.