Analog Fault Diagnosis Using Conic Optimization and Ellipsoidal Classifiers

This paper introduces a new fault diagnosis strategy for analog circuits based on conic optimization and ellipsoidal classifiers. Ellipsoidal classifiers are trained for efficient and accurate fault classification of the circuit under test (CUT). In the testing phase, the output of the ellipsoidal classifiers is used to isolate the actual CUT fault. The constructed classifiers exhibit high classification rate with competitive computational complexity even if the CUT has overlapping faults. Experimental results demonstrate the superior performance of the ellipsoidal classifiers in analog fault diagnosis.

[1]  M. A. El-Gamal,et al.  Genetically Evolved Neural Networks for Fault Classification in Analog Circuits , 2002, Neural Computing & Applications.

[2]  M. A. El-Gamal,et al.  Ensembles of Neural Networks for Fault Diagnosis in Analog Circuits , 2007, J. Electron. Test..

[3]  Mohamed A. El-Gamal,et al.  A knowledge-based approach for fault detection and isolation in analog circuits , 1997, Proceedings of International Conference on Neural Networks (ICNN'97).

[4]  Camelia Hora,et al.  Diagnosis of Local Spot Defects in Analog Circuits , 2012, IEEE Transactions on Instrumentation and Measurement.

[5]  Shambhu Upadhyaya,et al.  Linear circuit fault diagnosis using neuromorphic analyzers , 1997 .

[6]  Michelle Karg,et al.  A comparison of PCA, KPCA and LDA for feature extraction to recognize affect in gait kinematics , 2009, 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops.

[7]  Farzan Aminian,et al.  Analog fault diagnosis of actual circuits using neural networks , 2002, IEEE Trans. Instrum. Meas..

[8]  Augusto Montisci,et al.  Neural network-based analog fault diagnosis using testability analysis , 2004, Neural Computing & Applications.

[9]  Salvador Mir,et al.  Fault diagnosis of analog circuits based on machine learning , 2010, 2010 Design, Automation & Test in Europe Conference & Exhibition (DATE 2010).

[10]  L. Brown,et al.  Interval Estimation for a Binomial Proportion , 2001 .

[11]  Mohamed Fathy Abu El-Yazeed,et al.  A Combined Clustering and Neural Network Approach for Analog Multiple Hard Fault Classification , 1999, J. Electron. Test..

[12]  Stephen J. Wright,et al.  Numerical Optimization , 2018, Fundamental Statistical Inference.

[13]  Marcantonio Catelani,et al.  Fault diagnosis of electronic analog circuits using a radial basis function network classifier , 2000 .

[14]  M. Abdulghafour,et al.  Fault isolation in analog circuits using a fuzzy inference system , 2003, Comput. Electr. Eng..