Details

Title

A Fast Classification Method of Faults in Power Electronic Circuits Based on Support Vector Machines

Journal title

Metrology and Measurement Systems

Yearbook

2017

Numer

No 4

Publication authors

Divisions of PAS

Nauki Techniczne

Publisher

Polish Academy of Sciences Committee on Metrology and Scientific Instrumentation

Date

2017

Identifier

ISSN 0860-8229

References

Masrur (2010), Intelligent diagnosis of open and short circuit faults in electric drive inverters for real - time applications, IET Power Electron, 12, 02. ; Khanniche (2004), Wavelet - fuzzy - based algorithm for condition monitoring of voltage source inverter, Electron Lett, 40, 04. ; Martins (2012), Fault detection and diagnosis of grid - connected power inverters using PCA and current mean value, Proc, 31, 5185. ; Burges (1998), tutorial on support vector machines for pattern recognition Data Min, Knowl Disc, 27, 121. ; Cui (2011), novel approach of analog circuit fault diagnosis using support vector machinesclassifier, Measurement, 44, 281. ; Platt (2000), Large margin DAGs for multi - class Classification in, Advances Neural Information Processing Systems, 29, 547. ; Gu (null), - parameter space partition for cost - sensitive, Proc, 2015. ; Murphey (2006), Model - based fault diagnosis in electric drives using machine learning, IEEE Trans, 11, 290. ; Gu (2016), Robust Regularization Path Algorithm for ν - Support Vector Classification Neural Learn, IEEE Trans Syst, doi.org/10.1109/TNNLS.2016.2527796 ; Khomfoi (2007), Fault diagnosis and reconfiguration for multilevel inverter drive using based techniques, AI IEEE Trans Ind Electron, 15, 2954. ; Potamianos (2014), Open - circuit fault diagnosis for matrix converter drives and remedial operation using carrier - based modulation methods, IEEE Trans Ind Electron, 1. ; Kim (2008), Fault diagnosis of three - phase PWM inverters using wavelet and, Proc, 17, 329. ; Metrol (null), Faults classification of power electronic circuits based on a support vector data description method, Meas Syst, 22, 2015. ; Xu (2014), fault diagnosis strategy of cascaded - bridge multilevel inverters, Proc, 22, 164. ; Fernao (2012), Fault detection and diagnosis of voltage source inverter using the current trajectory mass center, Proc IEEE, 737. ; Diallo (2005), Fault detection and diagnosis in an induction machine drive : a pattern recognition approach based on Concordia stator mean current vector, IEEE Trans Energy, 20, 03. ; Mallat (1989), theory for multi - resolution signal decomposition : the wavelet representation Pattern, IEEE Trans Anal, 11, 674. ; Biet (2013), Rotor faults diagnosis using feature selection and nearest neighbors rule : application to a turbogenerator, IEEE Trans Ind Electron, 30, 4063. ; Hsu (2002), comparison of methods for multi - class support vector machines Neural, IEEE Trans, 19, 415. ; Ma (2009), Fault diagnosis of power electronic system based on fault gradation and neural network group, Neurocomputing, 13, 13. ; Fan (2010), Three - phase inverter fault diagnosis based on optimized neural networks, Proc, 16, 482. ; Wen (null), rapid learning algorithm for vehicle classification, Inform Sciences, 39. ; Wang (2013), fault diagnosis method for three - phase rectifiers, Int J Elec Power, 20, 266. ; Cui (2011), Analog circuit fault classification using improved one - against - one support vector machines, Meas Syst, 18, 37. ; Huang (2013), Data - based inverter IGBT open - circuit fault diagnosis in vector control induction motor drives, Proc IEEE, 24, 1039. ; Wang (2012), Application of transform in fault diagnosis of power electronics circuits, Scientia Iranica, 21, 721. ; Hu (2011), Fault classification method for inverter based on hybrid support vector machines and wavelet analysis, Int J Control Autom Syst, 23, 797. ; Vapnik (1999), An overview of statistical learning theory Neural, IEEE Trans, 26, 988. ; Chapelle (1999), Support Vector Machines for histogram - based image classification Neural, IEEE Trans, 28, 1055. ; Khomfoi (2007), Fault diagnostic system for a multilevel inverter using a neural network, IEEE Trans Power Electron, 22, 1062. ; Cortes (1995), Support vector networks, Learn, 25, 273. ; Delpha (2012), based diagnosis of inverter fed induction machine drive : a new challenge, Proc, 18, 3931. ; Filippetti (2000), Recent developments of induction motor drives fault diagnosis using AI techniques, IEEE Trans Ind Electron, 47, 994. ; Kadri (2013), Neural classification method in fault detection and diagnosis for voltage source inverter in variable speed drive with induction motor EVER, Proc, 11, 1. ; Lu (2009), literature of IGBT fault diagnostic and protection methods for power inverters, review IEEE Trans Ind Appl, 14, 777.

DOI

10.1515/mms-2017-0056

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