Zacytuj

H. Akçay and E. Germen. Subspace-based identification of acoustic noise spectra in induction motors. IEEE Transactions on Energy Conversion, 30(1):32–40, 2015.AkçayH.GermenE.Subspace-based identification of acoustic noise spectra in induction motorsIEEE Transactions on Energy Conversion3013240201510.1109/TEC.2014.2334633Search in Google Scholar

J. Antonino-Daviu, M. Riera-Guasp, J. Roger-Folch, F. Martínez-Giménez, and A. Peris. Application and optimization of the discrete wavelet transform for the detection of broken rotor bars in induction machines. Applied and Computational Harmonic Analysis, 21(2):268–279, 2006.Antonino-DaviuJ.Riera-GuaspM.Roger-FolchJ.Martínez-GiménezF.PerisA.Application and optimization of the discrete wavelet transform for the detection of broken rotor bars in induction machinesApplied and Computational Harmonic Analysis212268279200610.1016/j.acha.2005.12.003Search in Google Scholar

N. Arthur and J. Penman. Induction machine condition monitoring with higher order spectra. IEEE Transactions on Industrial Electronics, 47(5):1031–1041, 2000.ArthurN.PenmanJ.Induction machine condition monitoring with higher order spectraIEEE Transactions on Industrial Electronics47510311041200010.1109/41.873211Search in Google Scholar

T. P. Banerjee and S. Das. Multi-sensor data fusion using support vector machine for motor fault detection. Information Sciences, 217:96–107, 2012.BanerjeeT. P.DasS.Multi-sensor data fusion using support vector machine for motor fault detectionInformation Sciences21796107201210.1016/j.ins.2012.06.016Search in Google Scholar

G. Bin, J. Gao, X. Li, and B. Dhillon. Early fault diagnosis of rotating machinery based on wavelet packets—empirical mode decomposition feature extraction and neural network. Mechanical Systems and Signal Processing, 27:696–711, 2012.BinG.GaoJ.LiX.DhillonB.Early fault diagnosis of rotating machinery based on wavelet packets—empirical mode decomposition feature extraction and neural networkMechanical Systems and Signal Processing27696711201210.1016/j.ymssp.2011.08.002Search in Google Scholar

B. Boashash, E. J. Powers, and A. M. Zoubir. Higher-order statistical signal processing. Longman Cheshire, 1995.BoashashB.PowersE. J.ZoubirA. M.Higher-order statistical signal processingLongman Cheshire1995Search in Google Scholar

A. Ceban, R. Pusca, and R. Romary. Eccentricity and broken rotor bars faults-effects on the external axial field. In The XIX International Conference on Electrical Machines-ICEM 2010, pages 1–6. IEEE, 2010.CebanA.PuscaR.RomaryR.Eccentricity and broken rotor bars faults-effects on the external axial fieldInThe XIX International Conference on Electrical Machines-ICEM 201016IEEE201010.1109/ICELMACH.2010.5608009Search in Google Scholar

I. Chernyavska and O. Vítek. Analysis of broken rotor bar fault in a squirrel-cage induction motor by means of stator current and stray flux measurement. In 2016 IEEE International Power Electronics and Motion Control Conference (PEMC), pages 532–537. IEEE, 2016.ChernyavskaI.VítekO.Analysis of broken rotor bar fault in a squirrel-cage induction motor by means of stator current and stray flux measurementIn2016 IEEE International Power Electronics and Motion Control Conference (PEMC)532537IEEE201610.1109/EPEPEMC.2016.7752052Search in Google Scholar

T. Chow and G. Fei. Three phase induction machines asymmetrical faults identification using bispectrum. IEEE Transactions on Energy Conversion, 10(4):688–693, 1995.ChowT.FeiG.Three phase induction machines asymmetrical faults identification using bispectrumIEEE Transactions on Energy Conversion104688693199510.1109/60.475840Search in Google Scholar

X. Dai and Z. Gao. From model, signal to knowledge: A data-driven perspective of fault detection and diagnosis. IEEE Transactions on Industrial Informatics, 9(4):2226–2238, 2013.DaiX.GaoZ.From model, signal to knowledge: A data-driven perspective of fault detection and diagnosisIEEE Transactions on Industrial Informatics9422262238201310.1109/TII.2013.2243743Search in Google Scholar

J. de Jesus Rangel-Magdaleno, H. Peregrina-Barreto, J. M. Ramirez-Cortes, P. Gomez-Gil, and R. Morales-Caporal. Fpga-based broken bars detection on induction motors under different load using motor current signature analysis and mathematical morphology. IEEE Transactions on Instrumentation and Measurement, 63(5):1032–1040, 2013.de Jesus Rangel-MagdalenoJ.Peregrina-BarretoH.Ramirez-CortesJ. M.Gomez-GilP.Morales-CaporalR.Fpga-based broken bars detection on induction motors under different load using motor current signature analysis and mathematical morphologyIEEE Transactions on Instrumentation and Measurement63510321040201310.1109/TIM.2013.2286931Search in Google Scholar

P. A. Delgado-Arredondo, D. Morinigo-Sotelo, R. A. Osornio-Rios, J. G. Avina-Cervantes, H. Rostro-Gonzalez, and R. de Jesus Romero-Troncoso. Methodology for fault detection in induction motors via sound and vibration signals. Mechanical Systems and Signal Processing, 83:568–589, 2017.Delgado-ArredondoP. A.Morinigo-SoteloD.Osornio-RiosR. A.Avina-CervantesJ. G.Rostro-GonzalezH.de Jesus Romero-TroncosoR.Methodology for fault detection in induction motors via sound and vibration signalsMechanical Systems and Signal Processing83568589201710.1016/j.ymssp.2016.06.032Search in Google Scholar

M. Drif and A. J. M. Cardoso. Stator fault diagnostics in squirrel cage three-phase induction motor drives using the instantaneous active and reactive power signature analyses. IEEE Transactions on Industrial Informatics, 10(2):1348–1360, 2014.DrifM.CardosoA. J. M.Stator fault diagnostics in squirrel cage three-phase induction motor drives using the instantaneous active and reactive power signature analysesIEEE Transactions on Industrial Informatics10213481360201410.1109/TII.2014.2307013Search in Google Scholar

L. Frosini, C. Harlişca, and L. Szabó. Induction machine bearing fault detection by means of statistical processing of the stray flux measurement. IEEE Transactions on Industrial Electronics, 62(3):1846–1854, 2014.FrosiniL.HarlişcaC.SzabóL.Induction machine bearing fault detection by means of statistical processing of the stray flux measurementIEEE Transactions on Industrial Electronics62318461854201410.1109/TIE.2014.2361115Search in Google Scholar

Z. Gao, C. Cecati, and S. X. Ding. A survey of fault diagnosis and fault-tolerant techniques—part i: Fault diagnosis with model-based and signal-based approaches. IEEE Transactions on Industrial Electronics, 62(6):3757–3767, 2015.GaoZ.CecatiC.DingS. X.A survey of fault diagnosis and fault-tolerant techniques—part i: Fault diagnosis with model-based and signal-based approachesIEEE Transactions on Industrial Electronics62637573767201510.1109/TIE.2015.2417501Search in Google Scholar

M. Geethanjali and H. Ramadoss. Fault diagnosis of induction motors using motor current signature analysis: A review. In Advanced Condition Monitoring and Fault Diagnosis of Electric Machines, pages 1–37. IGI Global, 2019.GeethanjaliM.RamadossH.Fault diagnosis of induction motors using motor current signature analysis: A reviewInAdvanced Condition Monitoring and Fault Diagnosis of Electric Machines137IGI Global201910.4018/978-1-5225-6989-3.ch001Search in Google Scholar

T. Ghanbari and A. Farjah. A magnetic leakage flux-based approach for fault diagnosis in electrical machines. IEEE Sensors Journal, 14(9):2981–2988, 2014.GhanbariT.FarjahA.A magnetic leakage flux-based approach for fault diagnosis in electrical machinesIEEE Sensors Journal14929812988201410.1109/JSEN.2014.2319175Search in Google Scholar

A. Glowacz. Acoustic based fault diagnosis of three-phase induction motor. Applied Acoustics, 137:82–89, 2018.GlowaczA.Acoustic based fault diagnosis of three-phase induction motorApplied Acoustics1378289201810.1016/j.apacoust.2018.03.010Search in Google Scholar

A. Glowacz, W. Glowacz, Z. Glowacz, and J. Kozik. Early fault diagnosis of bearing and stator faults of the single-phase induction motor using acoustic signals. Measurement, 113:1–9, 2018.GlowaczA.GlowaczW.GlowaczZ.KozikJ.Early fault diagnosis of bearing and stator faults of the single-phase induction motor using acoustic signalsMeasurement11319201810.1016/j.measurement.2017.08.036Search in Google Scholar

T. Goktas, M. Zafarani, K. W. Lee, B. Akin, and T. Sculley. Comprehensive analysis of magnet defect fault monitoring through leakage flux. IEEE Transactions on Magnetics, 53(4):1–10, 2016.GoktasT.ZafaraniM.LeeK. W.AkinB.SculleyT.Comprehensive analysis of magnet defect fault monitoring through leakage fluxIEEE Transactions on Magnetics534110201610.1109/TMAG.2016.2617318Search in Google Scholar

K. C. Gryllias and I. A. Antoniadis. A support vector machine approach based on physical model training for rolling element bearing fault detection in industrial environments. Engineering Applications of Artificial Intelligence, 25(2):326–344, 2012.GrylliasK. C.AntoniadisI. A.A support vector machine approach based on physical model training for rolling element bearing fault detection in industrial environmentsEngineering Applications of Artificial Intelligence252326344201210.1016/j.engappai.2011.09.010Search in Google Scholar

F. Gu, Y. Shao, N. Hu, A. Naid, and A. Ball. Electrical motor current signal analysis using a modified bispectrum for fault diagnosis of downstream mechanical equipment. Mechanical Systems and Signal Processing, 25(1):360–372, 2011.GuF.ShaoY.HuN.NaidA.BallA.Electrical motor current signal analysis using a modified bispectrum for fault diagnosis of downstream mechanical equipmentMechanical Systems and Signal Processing251360372201110.1016/j.ymssp.2010.07.004Search in Google Scholar

C. Harlişca, L. Szabó, L. Frosini, and A. Albini. Diagnosis of rolling bearings faults in electric machines through stray magnetic flux monitoring. In 2013 8TH International Symposium on Advanced Topics in Electrical Engineering (Atee), pages 1–6. IEEE, 2013.HarlişcaC.SzabóL.FrosiniL.AlbiniA.Diagnosis of rolling bearings faults in electric machines through stray magnetic flux monitoringIn2013 8TH International Symposium on Advanced Topics in Electrical Engineering (Atee)16IEEE201310.1109/ATEE.2013.6563406Search in Google Scholar

R. Hoppler and R. A. Errath. Motor bearings, not must a piece of metal. In 2007 IEEE Cement Industry Technical Conference Record, pages 214–233. IEEE, 2007.HopplerR.ErrathR. A.Motor bearings, not must a piece of metalIn2007 IEEE Cement Industry Technical Conference Record214233IEEE200710.1109/CITCON.2007.359000Search in Google Scholar

R. M. Howard. Principles of random signal analysis and low noise design: The power spectral density and its applications. John Wiley & Sons, 2004.HowardR. M.Principles of random signal analysis and low noise design: The power spectral density and its applicationsJohn Wiley & Sons2004Search in Google Scholar

J.-N. Hwang and Y. H. Hu. Handbook of neural network signal processing. CRC press, 2001.HwangJ.-N.HuY. H.Handbook of neural network signal processingCRC press2001Search in Google Scholar

M. E. Iglesias-Martínez, J. A. Antonino-Daviu, P. Fernández de Córdoba, and J. A. Conejero. Rotor fault detection in induction motors based on time-frequency analysis using the bispectrum and the autocovariance of stray flux signals. Energies, 12(4):597, 2019.Iglesias-MartínezM. E.Antonino-DaviuJ. A.Fernández de CórdobaP.ConejeroJ. A.Rotor fault detection in induction motors based on time-frequency analysis using the bispectrum and the autocovariance of stray flux signalsEnergies124597201910.3390/en12040597Search in Google Scholar

M. E. Iglesias-Martinez, P. F. de Cordoba, J. Antonino-Daviu, and J. A. Conejero. Detection of nonadjacent rotor faults in induction motors via spectral subtraction and autocorrelation of stray flux signals. IEEE Transactions on Industry Applications, 55(5):4585–4594, 2019.Iglesias-MartinezM. E.de CordobaP. F.Antonino-DaviuJ.ConejeroJ. A.Detection of nonadjacent rotor faults in induction motors via spectral subtraction and autocorrelation of stray flux signalsIEEE Transactions on Industry Applications55545854594201910.1109/TIA.2019.2917861Search in Google Scholar

M. E. Iglesias-Martínez, P. F. de Córdoba, J. A. Antonino-Daviu, and J. A. Conejero. Detection of bar breakages in induction motor via spectral subtraction of stray flux signals. In 2018 XIII International Conference on Electrical Machines (ICEM), pages 1796–1802. IEEE, 2018.Iglesias-MartínezM. E.de CórdobaP. F.Antonino-DaviuJ. A.ConejeroJ. A.Detection of bar breakages in induction motor via spectral subtraction of stray flux signalsIn2018 XIII International Conference on Electrical Machines (ICEM)17961802IEEE201810.1109/ICELMACH.2018.8507078Search in Google Scholar

M. E. Iglesias-Martínez, P. F. de Córdoba, J. A. Antonino-Daviu, and J. A. Conejero. Detection of adjacent and non-adjacent bar breakages in induction motors via convolutional analysis of sound signals. Preprint, 2020.Iglesias-MartínezM. E.de CórdobaP. F.Antonino-DaviuJ. A.ConejeroJ. A.Detection of adjacent and non-adjacent bar breakages in induction motors via convolutional analysis of sound signalsPreprint202010.3390/app10196641Search in Google Scholar

F. Immovilli, A. Bellini, R. Rubini, and C. Tassoni. Diagnosis of bearing faults in induction machines by vibration or current signals: A critical comparison. IEEE Transactions on Industry Applications, 46(4):1350–1359, 2010.ImmovilliF.BelliniA.RubiniR.TassoniC.Diagnosis of bearing faults in induction machines by vibration or current signals: A critical comparisonIEEE Transactions on Industry Applications46413501359201010.1109/TIA.2010.2049623Search in Google Scholar

C. Jiang, S. Li, and T. G. Habetler. A review of condition monitoring of induction motors based on stray flux. In 2017 IEEE Energy Conversion Congress and Exposition (ECCE), pages 5424–5430. IEEE, 2017.JiangC.LiS.HabetlerT. G.A review of condition monitoring of induction motors based on stray fluxIn2017 IEEE Energy Conversion Congress and Exposition (ECCE)54245430IEEE201710.1109/ECCE.2017.8096907Search in Google Scholar

L. Jiang, Y. Liu, X. Li, and S. Tang. Using bispectral distribution as a feature for rotating machinery fault diagnosis. Measurement, 44(7):1284–1292, 2011.JiangL.LiuY.LiX.TangS.Using bispectral distribution as a feature for rotating machinery fault diagnosisMeasurement44712841292201110.1016/j.measurement.2011.03.024Search in Google Scholar

Q. Jiang and F. Chang. A novel rolling-element bearing faults classification method combines lower-order moment spectra and support vector machine. Journal of Mechanical Science and Technology, 33(4):1535–1543, 2019.JiangQ.ChangF.A novel rolling-element bearing faults classification method combines lower-order moment spectra and support vector machineJournal of Mechanical Science and Technology33415351543201910.1007/s12206-019-0305-2Search in Google Scholar

X. Jin and T. W. Chow. Anomaly detection of cooling fan and fault classification of induction motor using mahalanobis–taguchi system. Expert Systems with Applications, 40(15):5787–5795, 2013.JinX.ChowT. W.Anomaly detection of cooling fan and fault classification of induction motor using mahalanobis–taguchi systemExpert Systems with Applications401557875795201310.1016/j.eswa.2013.04.024Search in Google Scholar

J. Józwik. Identification and monitoring of noise sources of CNC machine tools by acoustic holography methods. Advances in Science and Technology Research Journal, 10(30), 2016.JózwikJ.Identification and monitoring of noise sources of CNC machine tools by acoustic holography methodsAdvances in Science and Technology Research Journal1030201610.12913/22998624/63386Search in Google Scholar

S. M. Kay. Fundamentals of statistical signal processing. Prentice Hall PTR, 1993.KayS. M.Fundamentals of statistical signal processingPrentice Hall PTR1993Search in Google Scholar

R. Liu, B. Yang, E. Zio, and X. Chen. Artificial intelligence for fault diagnosis of rotating machinery: A review. Mechanical Systems and Signal Processing, 108:33–47, 2018.LiuR.YangB.ZioE.ChenX.Artificial intelligence for fault diagnosis of rotating machinery: A reviewMechanical Systems and Signal Processing1083347201810.1016/j.ymssp.2018.02.016Search in Google Scholar

Z. Liu, H. Cao, X. Chen, Z. He, and Z. Shen. Multi-fault classification based on wavelet svm with pso algorithm to analyze vibration signals from rolling element bearings. Neurocomputing, 99:399–410, 2013.LiuZ.CaoH.ChenX.HeZ.ShenZ.Multi-fault classification based on wavelet svm with pso algorithm to analyze vibration signals from rolling element bearingsNeurocomputing99399410201310.1016/j.neucom.2012.07.019Search in Google Scholar

J. M. Mendel. Tutorial on higher-order statistics (spectra) in signal processing and system theory: Theoretical results and some applications. Proceedings of the IEEE, 79(3):278–305, 1991.MendelJ. M.Tutorial on higher-order statistics (spectra) in signal processing and system theory: Theoretical results and some applicationsProceedings of the IEEE793278305199110.1109/5.75086Search in Google Scholar

M. Mrugalski, M. Witczak, and J. Korbicz. Confidence estimation of the multi-layer perceptron and its application in fault detection systems. Engineering Applications of Artificial Intelligence, 21(6):895–906, 2008.MrugalskiM.WitczakM.KorbiczJ.Confidence estimation of the multi-layer perceptron and its application in fault detection systemsEngineering Applications of Artificial Intelligence216895906200810.1016/j.engappai.2007.09.008Search in Google Scholar

V. Muralidharan and V. Sugumaran. A comparative study of naïve bayes classifier and bayes net classifier for fault diagnosis of monoblock centrifugal pump using wavelet analysis. Applied Soft Computing, 12(8):2023–2029, 2012.MuralidharanV.SugumaranV.A comparative study of naïve bayes classifier and bayes net classifier for fault diagnosis of monoblock centrifugal pump using wavelet analysisApplied Soft Computing12820232029201210.1016/j.asoc.2012.03.021Search in Google Scholar

Y. Ono, Y. Onishi, T. Koshinaka, S. Takata, and O. Hoshuyama. Anomaly detection of motors with feature emphasis using only normal sounds. In Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on, pages 2800–2804. IEEE, 2013.OnoY.OnishiY.KoshinakaT.TakataS.HoshuyamaO.Anomaly detection of motors with feature emphasis using only normal soundsInAcoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on28002804IEEE201310.1109/ICASSP.2013.6638167Search in Google Scholar

R. H. C. Palácios, I. N. da Silva, A. Goedtel, and W. F. Godoy. A comprehensive evaluation of intelligent classifiers for fault identification in three-phase induction motors. Electric Power Systems Research, 127:249–258, 2015.PaláciosR. H. C.da SilvaI. N.GoedtelA.GodoyW. F.A comprehensive evaluation of intelligent classifiers for fault identification in three-phase induction motorsElectric Power Systems Research127249258201510.1016/j.epsr.2015.06.008Search in Google Scholar

P. Panagiotou, I. Arvanitakis, N. Lophitis, J. A. Antonino-Daviu, and K. N. Gyftakis. Analysis of stray flux spectral components in induction machines under rotor bar breakages at various locations. In 2018 XIII International Conference on Electrical Machines (ICEM), pages 2345–2351. IEEE, 2018.PanagiotouP.ArvanitakisI.LophitisN.Antonino-DaviuJ. A.GyftakisK. N.Analysis of stray flux spectral components in induction machines under rotor bar breakages at various locationsIn2018 XIII International Conference on Electrical Machines (ICEM)23452351IEEE201810.1109/ICELMACH.2018.8506929Search in Google Scholar

P. A. Panagiotou, I. Arvanitakis, N. Lophitis, J. Antonino-Daviu, and K. N. Gyftakis. A new approach for broken rotor bar detection in induction motors using frequency extraction in stray flux signals. IEEE Transactions on Industry Applications, 2019.PanagiotouP. A.ArvanitakisI.LophitisN.Antonino-DaviuJ.GyftakisK. N.A new approach for broken rotor bar detection in induction motors using frequency extraction in stray flux signalsIEEE Transactions on Industry Applications201910.1109/TIA.2019.2905803Search in Google Scholar

K. Pandey, P. Zope, and S. Suralkar. Review on fault diagnosis in three-phase induction motor. MEDHA–2012, Proceedings published by International Journal of Computer Applications (IJCA), 2012.PandeyK.ZopeP.SuralkarS.Review on fault diagnosis in three-phase induction motorMEDHA–2012, Proceedings published by International Journal of Computer Applications (IJCA)2012Search in Google Scholar

J. Rafiee, F. Arvani, A. Harifi, and M. Sadeghi. Intelligent condition monitoring of a gearbox using artificial neural network. Mechanical systems and signal processing, 21(4):1746–1754, 2007.RafieeJ.ArvaniF.HarifiA.SadeghiM.Intelligent condition monitoring of a gearbox using artificial neural networkMechanical systems and signal processing21417461754200710.1016/j.ymssp.2006.08.005Search in Google Scholar

A. Sadeghian, Z. Ye, and B. Wu. Online detection of broken rotor bars in induction motors by wavelet packet decomposition and artificial neural networks. IEEE Transactions on Instrumentation and Measurement, 58(7):2253–2263, 2009.SadeghianA.YeZ.WuB.Online detection of broken rotor bars in induction motors by wavelet packet decomposition and artificial neural networksIEEE Transactions on Instrumentation and Measurement58722532263200910.1109/TIM.2009.2013743Search in Google Scholar

L. Saidi, J. B. Ali, and F. Fnaiech. Application of higher order spectral features and support vector machines for bearing faults classification. ISA transactions, 54:193–206, 2015.SaidiL.AliJ. B.FnaiechF.Application of higher order spectral features and support vector machines for bearing faults classificationISA transactions54193206201510.1016/j.isatra.2014.08.007Search in Google Scholar

L. Saidi, F. Fnaiech, G. Capolino, and H. Henao. Stator current bi-spectrum patterns for induction machines multiple-faults detection. In IECON 2012-38th Annual Conference on IEEE Industrial Electronics Society, pages 5132–5137. IEEE, 2012.SaidiL.FnaiechF.CapolinoG.HenaoH.Stator current bi-spectrum patterns for induction machines multiple-faults detectionInIECON 2012-38th Annual Conference on IEEE Industrial Electronics Society51325137IEEE201210.1109/IECON.2012.6388975Search in Google Scholar

L. Saidi, F. Fnaiech, H. Henao, G. Capolino, and G. Cirrincione. Diagnosis of broken-bars fault in induction machines using higher order spectral analysis. ISA Transactions, 52(1):140–148, 2013.SaidiL.FnaiechF.HenaoH.CapolinoG.CirrincioneG.Diagnosis of broken-bars fault in induction machines using higher order spectral analysisISA Transactions521140148201310.1016/j.isatra.2012.08.003Search in Google Scholar

M. Salah, K. Bacha, and A. Chaari. An improved spectral analysis of the stray flux component for the detection of air-gap irregularities in squirrel cage motors. ISA transactions, 53(3):816–826, 2014.SalahM.BachaK.ChaariA.An improved spectral analysis of the stray flux component for the detection of air-gap irregularities in squirrel cage motorsISA transactions533816826201410.1016/j.isatra.2014.02.001Search in Google Scholar

B. Samanta. Gear fault detection using artificial neural networks and support vector machines with genetic algorithms. Mechanical systems and signal processing, 18(3):625–644, 2004.SamantaB.Gear fault detection using artificial neural networks and support vector machines with genetic algorithmsMechanical systems and signal processing183625644200410.1016/S0888-3270(03)00020-7Search in Google Scholar

P. Sangeetha and S. Hemamalini. Dyadic wavelet transform-based acoustic signal analysis for torque prediction of a three-phase induction motor. IET Signal Processing, 11(5):604–612, 2017.SangeethaP.HemamaliniS.Dyadic wavelet transform-based acoustic signal analysis for torque prediction of a three-phase induction motorIET Signal Processing115604612201710.1049/iet-spr.2016.0165Search in Google Scholar

J. Sanz, R. Perera, and C. Huerta. Gear dynamics monitoring using discrete wavelet transformation and multi-layer perceptron neural networks. Applied Soft Computing, 12(9):2867–2878, 2012.SanzJ.PereraR.HuertaC.Gear dynamics monitoring using discrete wavelet transformation and multi-layer perceptron neural networksApplied Soft Computing12928672878201210.1016/j.asoc.2012.04.003Search in Google Scholar

Z. Shen, X. Chen, X. Zhang, and Z. He. A novel intelligent gear fault diagnosis model based on emd and multi-class tsvm. Measurement, 45(1):30–40, 2012.ShenZ.ChenX.ZhangX.HeZ.A novel intelligent gear fault diagnosis model based on emd and multi-class tsvmMeasurement4513040201210.1016/j.measurement.2011.10.008Search in Google Scholar

A. Singhal and M. A. Khandekar. Bearing fault detection in induction motor using fast fourier transform. In IEEE Int. Conf. on Advanced Research in Engineering & Technology, 2013.SinghalA.KhandekarM. A.Bearing fault detection in induction motor using fast fourier transformInIEEE Int. Conf. on Advanced Research in Engineering & Technology2013Search in Google Scholar

A. Soualhi, K. Medjaher, and N. Zerhouni. Bearing health monitoring based on hilbert–huang transform, support vector machine, and regression. IEEE Transactions on Instrumentation and Measurement, 64(1):52–62, 2014.SoualhiA.MedjaherK.ZerhouniN.Bearing health monitoring based on hilbert–huang transform, support vector machine, and regressionIEEE Transactions on Instrumentation and Measurement6415262201410.1109/TIM.2014.2330494Search in Google Scholar

A. Swami, G. B. Giannakis, and G. Zhou. Bibliography on higher-order statistics. Signal processing, 60(1):65–126, 1997.SwamiA.GiannakisG. B.ZhouG.Bibliography on higher-order statisticsSignal processing60165126199710.1016/S0165-1684(97)00065-0Search in Google Scholar

O. Vitek, M. Janda, and V. Hajek. Effects of eccentricity on external magnetic field of induction machine. In Melecon 2010–2010 15th IEEE Mediterranean Electrotechnical Conference, pages 939–943. IEEE, 2010.VitekO.JandaM.HajekV.Effects of eccentricity on external magnetic field of induction machineInMelecon 2010–2010 15th IEEE Mediterranean Electrotechnical Conference939943IEEE201010.1109/MELCON.2010.5475925Search in Google Scholar

H. Wang, X. Bao, C. Di, and Z. Cheng. Detection of eccentricity fault using slot leakage flux monitoring. In 2015 9th International Conference on Power Electronics and ECCE Asia (ICPE-ECCE Asia), pages 2188–2193. IEEE, 2015.WangH.BaoX.DiC.ChengZ.Detection of eccentricity fault using slot leakage flux monitoringIn2015 9th International Conference on Power Electronics and ECCE Asia (ICPE-ECCE Asia)21882193IEEE201510.1109/ICPE.2015.7168080Search in Google Scholar

Y. Wang, J. Xiang, R. Markert, and M. Liang. Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: A review with applications. Mechanical Systems and Signal Processing, 66:679–698, 2016.WangY.XiangJ.MarkertR.LiangM.Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: A review with applicationsMechanical Systems and Signal Processing66679698201610.1016/j.ymssp.2015.04.039Search in Google Scholar

Z. Wang and C. Chang. Online fault detection of induction motors using frequency domain independent components analysis. In 2011 IEEE International Symposium on Industrial Electronics, pages 2132–2137. IEEE, 2011.WangZ.ChangC.Online fault detection of induction motors using frequency domain independent components analysisIn2011 IEEE International Symposium on Industrial Electronics21322137IEEE201110.1109/ISIE.2011.5984490Search in Google Scholar

Z. Wang, C. Chang, and Y. Zhang. A feature based frequency domain analysis algorithm for fault detection of induction motors. In 2011 6th IEEE Conference on Industrial Electronics and Applications, pages 27–32. IEEE, 2011.WangZ.ChangC.ZhangY.A feature based frequency domain analysis algorithm for fault detection of induction motorsIn2011 6th IEEE Conference on Industrial Electronics and Applications2732IEEE201110.1109/ICIEA.2011.5975545Search in Google Scholar

W. Wenbing and X. Jinquan. The application of coupled three order cumulants’ differential feature in fault diagnosis. In 2017 International Conference on Virtual Reality and Visualization (ICVRV), pages 374–375. IEEE, 2017.WenbingW.JinquanX.The application of coupled three order cumulants’ differential feature in fault diagnosisIn2017 International Conference on Virtual Reality and Visualization (ICVRV)374375IEEE201710.1109/ICVRV.2017.00085Search in Google Scholar

I. Zamudio-Ramirez, R. A. Osornio-Rios, M. Trejo-Hernandez, R. d. J. Romero-Troncoso, and J. A. Antonino-Daviu. Smart-sensors to estimate insulation health in induction motors via analysis of stray flux. Energies, 12(9):1658, 2019.Zamudio-RamirezI.Osornio-RiosR. A.Trejo-HernandezM.Romero-TroncosoR. d. J.Antonino-DaviuJ. A.Smart-sensors to estimate insulation health in induction motors via analysis of stray fluxEnergies1291658201910.3390/en12091658Search in Google Scholar

X. Zhang and J. Zhou. Multi-fault diagnosis for rolling element bearings based on ensemble empirical mode decomposition and optimized support vector machines. Mechanical Systems and Signal Processing, 41(1–2):127–140, 2013.ZhangX.ZhouJ.Multi-fault diagnosis for rolling element bearings based on ensemble empirical mode decomposition and optimized support vector machinesMechanical Systems and Signal Processing411–2127140201310.1016/j.ymssp.2013.07.006Search in Google Scholar

W. Zhao, T. Tao, and E. Zio. System reliability prediction by support vector regression with analytic selection and genetic algorithm parameters selection. Applied Soft Computing, 30:792–802, 2015.ZhaoW.TaoT.ZioE.System reliability prediction by support vector regression with analytic selection and genetic algorithm parameters selectionApplied Soft Computing30792802201510.1016/j.asoc.2015.02.026Search in Google Scholar

W. Zhao, Y. Zhang, and Y. Zhu. Diagnosis for transformer faults based on combinatorial Bayes Network. In 2009 2nd International Congress on Image and Signal Processing, pages 1–3. IEEE, 2009.ZhaoW.ZhangY.ZhuY.Diagnosis for transformer faults based on combinatorial Bayes NetworkIn2009 2nd International Congress on Image and Signal Processing13IEEE200910.1109/CISP.2009.5301965Search in Google Scholar

F. Zidat, J.-P. Lecointe, F. Morganti, J.-F. Brudny, T. Jacq, and F. Streiff. Non invasive sensors for monitoring the efficiency of ac electrical rotating machines. Sensors, 10(8):7874–7895, 2010.ZidatF.LecointeJ.-P.MorgantiF.BrudnyJ.-F.JacqT.StreiffF.Non invasive sensors for monitoring the efficiency of ac electrical rotating machinesSensors10878747895201010.3390/s100807874323115622163631Search in Google Scholar

eISSN:
2444-8656
Język:
Angielski
Częstotliwość wydawania:
Volume Open
Dziedziny czasopisma:
Life Sciences, other, Mathematics, Applied Mathematics, General Mathematics, Physics