Details

Title

A Novel Approach To Diagnosis Of Analog Circuit Incipient Faults Based On KECA And OAO LSSVM

Journal title

Metrology and Measurement Systems

Yearbook

2015

Volume

vol. 22

Issue

No 2

Authors

Keywords

analog circuits ; incipient fault diagnosis ; wavelet transform ; kernel entropy component analysis ; least squares support vector machine

Divisions of PAS

Nauki Techniczne

Coverage

251-262

Publisher

Polish Academy of Sciences Committee on Metrology and Scientific Instrumentation

Date

2015[2015.01.01 AD - 2015.12.31 AD]

Type

Artykuły / Articles

Identifier

DOI: 10.1515/mms-2015-0025 ; ISSN 2080-9050, e-ISSN 2300-1941

Source

Metrology and Measurement Systems; 2015; vol. 22; No 2; 251-262

References

He (2004), Wavelet neural network approach for fault diagnosis of analogue circuits Devices, Proc Inst Elect Eng Circuits Syst, 151, 379, doi.org/10.1049/ip-cds:20040495 ; Gomez (2012), Kernel Entropy Component Analysis for Remote Sensing Image Clustering, IEEE Geosci Remote S, 9, 312, doi.org/10.1109/LGRS.2011.2167212 ; Xiao (2011), A novel approach for analog fault diagnosis based on neural networks and improved kernel PCA, Neurocomputing, 74, 1102, doi.org/10.1016/j.neucom.2010.12.003 ; Pułka (2011), Two heuristic algorithms for test point selection in analog circuit diagnoses, Metrol Meas Syst, 18, 115, doi.org/10.2478/v10178-011-0011-6 ; Cortes (1995), Support - vector networks, Mach Learn, 20, 273, doi.org/10.1007/BF00994018 ; Xiao (2010), A linear ridgelet network approach for fault diagnosis of analog circuit Inf, Sci China Sci, 53, 2251, doi.org/10.1007/s11432-010-4077-7 ; Xiao (2012), A novel neural - network approach of analog fault diagnosis based on kernel discriminant analysis and particle swarm optimization, Appl Soft Comput, 12, 904, doi.org/10.1016/j.asoc.2011.10.002 ; Grzechca (2009), Fault diagnosis in analog electronic circuits - the SVM approach, Metrol Meas Syst, 16, 583. ; Toczek (2005), A neural network based system for soft fault diagnosis in electronic circuits, Metrol Meas Syst, 12, 463. ; Cui (2010), A novel approach of analog fault classification using a support vector machines classifier, Metrol Meas Syst, 17, 561, doi.org/10.2478/v10178-010-0046-0 ; Aminian (2002), Analog fault diagnosis of actual circuits using neural networks, IEEE Trans Instrum Meas, 51, 544, doi.org/10.1109/TIM.2002.1017726 ; Aminian (2000), Neural - network based analog - circuit fault diagnosis using wavelet transform as preprocessor II Analog Digit, IEEE Trans Circuits Syst Signal Process, 47, 151. ; Suykens (1999), Least squares support vector machine classifiers, Neural Process Lett, 9, 293, doi.org/10.1023/A:1018628609742 ; Spina (1997), Linear circuit fault diagnosis using neuromorphic analyzers II Analog Digit, IEEE Trans Circuits Syst Signal Process, 44, 188. ; Aminian (2007), A modular fault - diagnostic system for analog electronic circuits using neural networks with wavelet transform as a preprocessor, IEEE Trans Instrum Meas, 56, 1546, doi.org/10.1109/TIM.2007.904549 ; Jenssen (2010), Kernel entropy component analysis, IEEE Pattern Anal, 32, 847, doi.org/10.1109/TPAMI.2009.100 ; Vasan (2013), Diagnostics and prognostics method for analog electronic circuits, IEEE TInd Electron, 60, 5277, doi.org/10.1109/TIE.2012.2224074 ; Xu (2010), A novel method for the diagnosis of the incipient faults in analog circuits based on LDA and HMM, Circuits Syst Signal Process, 29, 577, doi.org/10.1007/s00034-010-9160-1 ; Ni (2011), An adaptive approach based on KPCA and SVM for real - time fault diagnosis of HVCBs, IEEE T Power Deliver, 26, 1960, doi.org/10.1109/TPWRD.2011.2136441 ; Shekar (2011), Face recognition using kernel entropy component analysis, Neurocomputing, 74, 1053, doi.org/10.1016/j.neucom.2010.10.012 ; Grzechca (2011), Soft fault clustering in analog electronic circuits with the use of self organizing neural network, Metrol Meas Syst, 18, 555, doi.org/10.2478/v10178-011-0054-8 ; Long (2012), Feature vector selection method using Mahalanobis distance for diagnostics of analog circuits based on LS, Electron Test, 28, 745, doi.org/10.1007/s10836-012-5301-8 ; Tan (2008), A novel method for analog fault diagnosis based on neural networks and genetic algorithms, IEEE Trans Instrum Meas, 57, 2631, doi.org/10.1109/TIM.2008.925009 ; Arizmendi (2012), Classification of human brain tumours from MRS data using Discrete Wavelet Transform and Bayesian Neural Networks, Expert Syst Appl, 39, 5223, doi.org/10.1016/j.eswa.2011.11.017 ; Yuan (2010), A new neural - network - based fault diagnosis approach for analog circuits by using kurtosis and entropy as a preprocessor, IEEE Trans Instrum Meas, 59, 586, doi.org/10.1109/TIM.2009.2025068 ; Aminian (2001), Fault diagnosis of analog circuits using Bayesian neural networks with wavelet transform as preprocessor, J Electron Test, 17, 29, doi.org/10.1023/A:1011141724916 ; Cui (2011), Analog circuit fault classification using improved one - against - one Support Vector Machines, Metrol Meas Syst, 18, 569, doi.org/10.2478/v10178-011-0055-7
×