Accesso libero

Fault Diagnosis and Prognosis of Bearing Based on Hidden Markov Model with Multi-Features

INFORMAZIONI SU QUESTO ARTICOLO

Cita

S. Dong and T. Luo, Bearing degradation process prediction based on the PCA and optimized LS-SVM model, Measurement, vol. 46, pp. 3143–3152, 2013, DOI: 10.1016/j.measurement.2013.06.038.DongS.LuoT.Bearing degradation process prediction based on the PCA and optimized LS-SVM modelMeasurement4631433152201310.1016/j.measurement.2013.06.038Open DOISearch in Google Scholar

H. Luo, L. Wei, Y. Wang, L. Wanga, X. Zhao. A novel approach for analog fault diagnosis based on stochastic signal analysis and improved GHMM. Measurement, 81, 26–35, 2016, DOI: 10.1016/j.measurement.2015.11.041.LuoH.WeiL.WangY.WangaL.ZhaoX.A novel approach for analog fault diagnosis based on stochastic signal analysis and improved GHMMMeasurement812635201610.1016/j.measurement.2015.11.041Open DOISearch in Google Scholar

S. Benmoussa, M.A. Djeziri, Experimental Application on a Mechanical Transmission System of Integrated Fault Diagnosis and Fault Prognosis method, IFAC-PapersOnLine, 51(24), 1016–1023, 2018, DOI: 10.1016/j.ifacol.2018.09.713.BenmoussaS.DjeziriM.A.Experimental Application on a Mechanical Transmission System of Integrated Fault Diagnosis and Fault Prognosis methodIFAC-PapersOnLine512410161023201810.1016/j.ifacol.2018.09.713Open DOISearch in Google Scholar

V.P, Santiago V, Raffaele; Ferrer, Alberto. Data-driven supervised fault diagnosis methods based on latent variable models: a comparative study. Chemometrics and Intelligent Laboratory Systems, 187, 41–52, 2019, DOI: 10.1016/j.chemolab.2019.02.006.SantiagoV.PRaffaeleVFerrerAlbertoData-driven supervised fault diagnosis methods based on latent variable models: a comparative studyChemometrics and Intelligent Laboratory Systems1874152201910.1016/j.chemolab.2019.02.006Open DOISearch in Google Scholar

S. Muhammad, K. Cheol-Hong, K. Jong-Myon. A Hybrid Feature Model and Deep-Learning-Based Bearing Fault Diagnosis. Sensors, 2017, 17(12):2876, DOI: 10.3390/s17122876.MuhammadS.Cheol-HongK.Jong-MyonK.A Hybrid Feature Model and Deep-Learning-Based Bearing Fault DiagnosisSensors20171712287610.3390/s17122876575149929232908Open DOISearch in Google Scholar

Y. Xiaoan, M. Jia. A novel optimized SVM classification algorithm with multi-domain feature and its application to fault diagnosis of rolling bearing. Neurocomputing 313(2018) 47–64, DOI: 10.1016/j.neucom.2018.05.002.XiaoanY.JiaM.A novel optimized SVM classification algorithm with multi-domain feature and its application to fault diagnosis of rolling bearingNeurocomputing3132018476410.1016/j.neucom.2018.05.002Open DOISearch in Google Scholar

X. Zhang, Z. Liu, J. Wang. Time-frequency analysis for bearing fault diagnosis using multiple Q-factor Gabor wavelets, ISA Transactions, 87, 225–234, 2018, DOI: 10.1016/j.isatra.2018.ZhangX.LiuZ.WangJ.Time-frequency analysis for bearing fault diagnosis using multiple Q-factor Gabor waveletsISA Transactions87225234201810.1016/j.isatra.2018Open DOISearch in Google Scholar

H. Huang, N. Baddour, M. Liang. Bearing fault diagnosis under unknown time-varying rotational speed conditions via multiple time-frequency curve?extraction. Journal of Sound and Vibration, 2018, 414:43–60, DOI: 10.1016/j.jsv.2017.11.005.HuangH.BaddourN.LiangM.Bearing fault diagnosis under unknown time-varying rotational speed conditions via multiple time-frequency curve?extractionJournal of Sound and Vibration2018414436010.1016/j.jsv.2017.11.005Open DOISearch in Google Scholar

G.F. Bin, J.J. Gao, X.J. Li, B.S. 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, DOI: 10.1016/j.ymssp.2011.08.002.BinG.F.GaoJ.J.LiX.J.DhillonB.S.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.2011.08.002Open DOISearch in Google Scholar

M.M. Manjurul Islam, J.M. Kim. Automated bearing fault diagnosis scheme using 2D representation of wavelet packet transform and deep convolutional neural network, Computers in Industry, 106, 142–1532, 2019, DOI: 10.1016/j.compind.2019.01.00.Manjurul IslamM.M.KimJ.M.Automated bearing fault diagnosis scheme using 2D representation of wavelet packet transform and deep convolutional neural networkComputers in Industry1061421532201910.1016/j.compind.2019.01.00Open DOISearch in Google Scholar

U. Lepik. Application of Wavelet Transform Techniques to Vibration Studies. Proc. Estonian Acad. Sci. Phys. Math., 2001, 50, 3, 155–168LepikU.Application of Wavelet Transform Techniques to Vibration StudiesProc. Estonian Acad. Sci. Phys. Math.200150315516810.3176/phys.math.2001.3.05Search in Google Scholar

A.K.S. Jardine, D. Lin, D. Banjevic, A review on machinery diagnostics and prognostics implementing condition-based maintenance, Mechanical Systems and Signal Processing 20, 1483–1510, 2006, DOI: 10.1016/j.ymssp.2005.09.012.JardineA.K.S.LinD.BanjevicD.A review on machinery diagnostics and prognostics implementing condition-based maintenanceMechanical Systems and Signal Processing2014831510200610.1016/j.ymssp.2005.09.012Open DOISearch in Google Scholar

V.T. Tran, B.S. Yang, A.C.C. Tan. Multi-step ahead direct prediction for the machine condition prognosis using regression trees and neuro-fuzzy systems, Expert Systems with Applications 36 9378–9387, 2009, DOI: 10.1016/j.eswa.2009.01.007.TranV.T.YangB.S.TanA.C.C.Multi-step ahead direct prediction for the machine condition prognosis using regression trees and neuro-fuzzy systemsExpert Systems with Applications3693789387200910.1016/j.eswa.2009.01.007Open DOISearch in Google Scholar

M. Roemer, J. Dzakowic, R. Orsagh, C. Byington, G. Vachtsevanos. An Overview of Selected Prognostic Technologies with Reference to an Integrated PHM Architecture. In Proceedings of the First International Forum on Integrated System Health Engineering and Management in Aerospace, Big Sky, UT, USA, 2005; pp. 3941–3947RoemerM.DzakowicJ.OrsaghR.ByingtonC.VachtsevanosG.An Overview of Selected Prognostic Technologies with Reference to an Integrated PHM ArchitectureInProceedings of the First International Forum on Integrated System Health Engineering and Management in AerospaceBig Sky, UT, USA200539413947Search in Google Scholar

M.A. Djeziri, S. Benmoussa, M.EH. Benbouzid, Data-driven approach augmented in simulation for robust fault prognosis, Engineering Applications of Artificial Intelligence, 86, 154–164, 2019, DOI: 10.1016/j.engappai.2019.09.002.DjeziriM.A.BenmoussaS.BenbouzidM.EH.Data-driven approach augmented in simulation for robust fault prognosisEngineering Applications of Artificial Intelligence86154164201910.1016/j.engappai.2019.09.002Open DOISearch in Google Scholar

A.K. Jain, J. Mao, K.M. Mohiuddin. Artificial neural networks: A tutorial. Computer, 29(3): 31–44, 1996, DOI: 10.1109/2.485891.JainA.K.MaoJ.MohiuddinK.M.Artificial neural networks: A tutorialComputer2933144199610.1109/2.485891Open DOISearch in Google Scholar

M.E. Tipping, The relevance vector machine. Advances in Information Processing System, 2, 652–658, 2000.TippingM.E.The relevance vector machineAdvances in Information Processing System26526582000Search in Google Scholar

N. Friedman, D. Geiger, M. Goldszmidt. Bayesian network classifiers. Machine learning, 29(2–3), 131–163, 1997, DOI: 10.1023/A:1007465528199.FriedmanN.GeigerD.GoldszmidtM.Bayesian network classifiersMachine learning292–3131163199710.1023/A:1007465528199Open DOISearch in Google Scholar

L.E. Baum, T. Petrie. Statistical inference for probabilistic functions of finite state Markov chains. The annals of mathematical statistics, 37(6), 1554–1563, 1966, DOI: 10.1214/aoms/1177699147.BaumL.E.PetrieT.Statistical inference for probabilistic functions of finite state Markov chainsThe annals of mathematical statistics37615541563196610.1214/aoms/1177699147Open DOISearch in Google Scholar

A.J. Smola, B. Schölkopf. A tutorial on support vector regression. Statistics and computing, 14(3), 199–222, 2004, DOI: 10.1023/B:STCO.0000035301.49549.88.SmolaA.J.SchölkopfB.A tutorial on support vector regressionStatistics and computing143199222200410.1023/B:STCO.0000035301.49549.88Open DOISearch in Google Scholar

T. Chuk, K. Crooke, W.G. Hayward, A.B. Chan, H.J. Hsiao. Hidden Markov model analysis reveals the advantage of analytic eye movement patterns in face recognition across cultures. Cognition, 169, 102–117, 2017, DOI: 10.1016/j.cognition.2017.08.003.ChukT.CrookeK.HaywardW.G.ChanA.B.HsiaoH.J.Hidden Markov model analysis reveals the advantage of analytic eye movement patterns in face recognition across culturesCognition169102117201710.1016/j.cognition.2017.08.00328869811Open DOISearch in Google Scholar

K.M. Sagayam, D.J. Hemanth. ABC algorithm based optimization of 1-D hidden Markov model for hand gesture recognition applications. Computers in Industry, 99, 313–323, 2018, DOI: 10.1016/j.compind.2018.03.035.SagayamK.M.HemanthD.J.ABC algorithm based optimization of 1-D hidden Markov model for hand gesture recognition applicationsComputers in Industry99313323201810.1016/j.compind.2018.03.035Open DOISearch in Google Scholar

J.T. Chien, H.C. Wang, Telephone speech recognition based on Bayesian adaptation of hidden Markov models, Speech Communication, 22(4), 369–384, 1997, DOI: 10.1016/S0167-6393(97)00033-2.ChienJ.T.WangH.C.Telephone speech recognition based on Bayesian adaptation of hidden Markov modelsSpeech Communication224369384199710.1016/S0167-6393(97)00033-2Open DOISearch in Google Scholar

S. Dong, T. Luo. Bearing degradation process prediction based on the PCA and optimized LS-SVM model. Measurement, 46: 3143–3152, 2013, DOI: 10.1016/j.measurement.2013.06.038.DongS.LuoT.Bearing degradation process prediction based on the PCA and optimized LS-SVM modelMeasurement4631433152201310.1016/j.measurement.2013.06.038Open DOISearch in Google Scholar

N.G. Nikolaou, I.A. Antoniadis. Rolling element bearing fault diagnosis using wavelet packets. NDT and E International, 2002, 35(3):197–205, DOI: 10.1016/S0963-8695(01)00044-5.NikolaouN.G.AntoniadisI.A.Rolling element bearing fault diagnosis using wavelet packetsNDT and E International200235319720510.1016/S0963-8695(01)00044-5Open DOISearch in Google Scholar

B. Asgarian, V. Aghaeidoost, H.R. Shokrgozar. Damage detection of jacket type offshore platforms using rate of signal energy using wavelet packet transform. Marine Structures, 45, 1–21, 2016, DOI: 10.1016/j.marstruc.2015.10.003.AsgarianB.AghaeidoostV.ShokrgozarH.R.Damage detection of jacket type offshore platforms using rate of signal energy using wavelet packet transformMarine Structures45121201610.1016/j.marstruc.2015.10.003Open DOISearch in Google Scholar

L.R. Rabiner, A tutorial on hidden Markov models and selected applications in speech recognition, Proceedings of the IEEE, 1989, 77:257–286.RabinerL.R.A tutorial on hidden Markov models and selected applications in speech recognitionProceedings of the IEEE19897725728610.1016/B978-0-08-051584-7.50027-9Search in Google Scholar

P. Baruah, R.B. Chinnam. HMMs for diagnostics and prognostics in machining processes. Int. J. Production Res. 2005, 43, 1275–1293, DOI: 10.1080/00207540412331327727.BaruahP.ChinnamR.B.HMMs for diagnostics and prognostics in machining processesInt. J. Production Res.2005431275129310.1080/00207540412331327727Open DOISearch in Google Scholar

L.E. Baum, T. Petrie, G. Soules, N, Weiss. A maximization technique occuring in the statistical analysis of probabilistic functions of Markov chains. Ann. Math. Stat., 41:164–171, 1970, DOI: 10.1214/aoms/1177697196.BaumL.E.PetrieT.SoulesG.WeissNA maximization technique occuring in the statistical analysis of probabilistic functions of Markov chainsAnn. Math. Stat.41164171197010.1214/aoms/1177697196Open DOISearch in Google Scholar

W. Zhao, L. Wang, An effective bacterial foraging optimizer for global optimization. Inf. Sci., 2016, 329: 719–735.ZhaoW.WangL.An effective bacterial foraging optimizer for global optimizationInf. Sci.201632971973510.1016/j.ins.2015.10.001Search in Google Scholar

W. Zhao, L. Wang, Z. Zhang, A novel atom search optimization for dispersion coefficient estimation in groundwater, Future Gener. Comput. Syst., 2019, 91: 601–610.ZhaoW.WangL.ZhangZ.A novel atom search optimization for dispersion coefficient estimation in groundwaterFuture Gener. Comput. Syst.20199160161010.1016/j.future.2018.05.037Search in Google Scholar

eISSN:
2444-8656
Lingua:
Inglese
Frequenza di pubblicazione:
Volume Open
Argomenti della rivista:
Life Sciences, other, Mathematics, Applied Mathematics, General Mathematics, Physics