Diagnosis of incipient failures in rolling element bearings under nonstationary operation conditions by vibration analysis

ilustraciones, diagramas

Autores:: Arango Castrillón, Juan David

Tipo de recurso:

Fecha de publicación:: 2023

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: eng

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repository_id_str
dc.title.eng.fl_str_mv	Diagnosis of incipient failures in rolling element bearings under nonstationary operation conditions by vibration analysis
dc.title.translated.spa.fl_str_mv	Diagnóstico de fallas incipientes en rodamientos bajo condiciones de operación no estacionarias mediante análisis de vibraciones
title	Diagnosis of incipient failures in rolling element bearings under nonstationary operation conditions by vibration analysis
spellingShingle	Diagnosis of incipient failures in rolling element bearings under nonstationary operation conditions by vibration analysis 530 - Física::534 - Sonido y vibraciones relacionadas 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería Ingeniería mecánica Vibración - Mediciones Rolling Element Bearing Vibration Analysis Non-stationary Speed Cyclo-non-stationary analysis Computer order tracking Spectral Kurtosis Rodamiento Análisis de Vibraciones Velocidad variable Cyclo-No Estacionareidad Order Tracking Computarizado Kurtosis Espectral
title_short	Diagnosis of incipient failures in rolling element bearings under nonstationary operation conditions by vibration analysis
title_full	Diagnosis of incipient failures in rolling element bearings under nonstationary operation conditions by vibration analysis
title_fullStr	Diagnosis of incipient failures in rolling element bearings under nonstationary operation conditions by vibration analysis
title_full_unstemmed	Diagnosis of incipient failures in rolling element bearings under nonstationary operation conditions by vibration analysis
title_sort	Diagnosis of incipient failures in rolling element bearings under nonstationary operation conditions by vibration analysis
dc.creator.fl_str_mv	Arango Castrillón, Juan David
dc.contributor.advisor.none.fl_str_mv	Guevara Carazas, Fernando Jesús Restrepo Martínez, Alejandro
dc.contributor.author.none.fl_str_mv	Arango Castrillón, Juan David
dc.contributor.researchgroup.spa.fl_str_mv	Gestión, Operación y Mantenimiento de Activos - Gomac
dc.contributor.orcid.spa.fl_str_mv	Restrepo Martínez, Alejandro [0000-0001-8978-2077] Arango Castrillón, Juan David [0000-0002-4615-9186] Guevara Carazas, Fernando Jesús [0000-0001-8529-4383]
dc.subject.ddc.spa.fl_str_mv	530 - Física::534 - Sonido y vibraciones relacionadas 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería
topic	530 - Física::534 - Sonido y vibraciones relacionadas 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería Ingeniería mecánica Vibración - Mediciones Rolling Element Bearing Vibration Analysis Non-stationary Speed Cyclo-non-stationary analysis Computer order tracking Spectral Kurtosis Rodamiento Análisis de Vibraciones Velocidad variable Cyclo-No Estacionareidad Order Tracking Computarizado Kurtosis Espectral
dc.subject.lemb.none.fl_str_mv	Ingeniería mecánica Vibración - Mediciones
dc.subject.proposal.eng.fl_str_mv	Rolling Element Bearing Vibration Analysis Non-stationary Speed Cyclo-non-stationary analysis Computer order tracking Spectral Kurtosis
dc.subject.proposal.spa.fl_str_mv	Rodamiento Análisis de Vibraciones Velocidad variable Cyclo-No Estacionareidad Order Tracking Computarizado Kurtosis Espectral
description	ilustraciones, diagramas
publishDate	2023
dc.date.accessioned.none.fl_str_mv	2023-07-27T16:44:03Z
dc.date.available.none.fl_str_mv	2023-07-27T16:44:03Z
dc.date.issued.none.fl_str_mv	2023-01-31
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv	Text
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TM
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/84318
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.repo.none.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/84318 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	eng
language	eng
dc.relation.indexed.spa.fl_str_mv	RedCol LaReferencia
dc.relation.references.spa.fl_str_mv	R. Potter and M. Gribler, “Computed order tracking obsoletes older methods,” SAE Tech. Pap., no. l, pp. 63–67, 1989. R. B. Randall and J. Antoni, “Rolling element bearing diagnostics---A tutorial,” Mech. Syst. Signal Process., vol. 25, no. 2, pp. 485–520, 2011. F. Bonnardot, R. B. Randall, and J. Antoni, “Enhanced unsupervised noise cancellation using angular resampling for planetary bearing fault diagnosis,” Int. J. Acoust. Vib., vol. 9, no. 2, pp. 51–60, 2004. P. D. McFadden, “Interpolation techniques for time domain averaging of gear vibration,” Mech. Syst. Signal Process., vol. 3, no. 1, pp. 87–97, 1989. C. Peeters et al., “Review and comparison of tacholess instantaneous speed estimation methods on experimental vibration data,” Mech. Syst. Signal Process., vol. 129, pp. 407–436, 2019. P. D. McFadden and J. D. Smith, “Model for the vibration produced by a single point defect in a rolling element bearing,” J. Sound Vib., vol. 96, no. 1, pp. 69–82, 1984. I. Howard, “A Review of Rolling Element Bearing Vibration ‘Detection, Diagnosis and Prognosis,’” DSTO-AMRL Report, DSTO-RR-00113, no. October 1994, pp. 35–41, 1994. B. P. Graney and K. Starry, “Rolling element bearing analysis,” Mater. Eval., vol. 70, no. 1, 2012. D. Abboud, M. Elbadaoui, W. A. Smith, and R. B. Randall, “Advanced bearing diagnostics: A comparative study of two powerful approaches,” Mechanical Systems and Signal Processing, vol. 114. pp. 604–627, 2019. D. Abboud, J. Antoni, M. Eltabach, and S. Sieg-Zieba, “Angle\time cyclostationarity for the analysis of rolling element bearing vibrations,” Meas. J. Int. Meas. Confed., vol. 75, pp. 29–39, 2015. N. K. Verma and T. S. S. Subramanian, “Cost benefit analysis of intelligent condition based maintenance of rotating machinery,” 2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA). 2012. B. Al-Najjar and I. Alsyouf, “Enhancing a company’s profitability and competitiveness using integrated vibration-based maintenance: A case study,” Eur. J. Oper. Res., vol. 157, no. 3, pp. 643–657, 2004. B. Al-Najjar, “The lack of maintenance and not maintenance which costs: A model to describe and quantify the impact of vibration-based maintenance on company’s business,” Int. J. Prod. Econ., vol. 107, no. 1, pp. 260–273, May 2007. R. B. Randall, Vibration-based Condition Monitoring: Industrial, Aerospace and Automotive Applications. John Wiley & Sons, 2011. P. D. McFadden and J. D. Smith, “Vibration monitoring of rolling element bearings by the high-frequency resonance technique --- a review,” Tribology International, vol. 17, no. 1. pp. 3–10, 1984. M. J. Dowling, “Application of non-stationary analysis to machinery monitoring,” in 1993 {IEEE} International Conference on Acoustics, Speech, and Signal Processing, 1993, vol. 1, pp. 59--62 vol.1. D. Rémond, J. Antoni, and R. B. Randall, “Editorial for the special issue on Instantaneous Angular Speed ({IAS}) processing and angular applications,” Mechanical Systems and Signal Processing, vol. 44, no. 1–2. pp. 1–4, 2014. {OREDA}: Offshore Reliability Data Handbook. OREDA Participants, 2009. M. El Hachemi Benbouzid, “A review of induction motors signature analysis as a medium for faults detection,” IEEE Trans. Ind. Electron., vol. 47, no. 5, pp. 984–993, 2000. R. R. Schoen, T. G. Habetler, F. Kamran, and R. G. Bartfield, “Motor bearing damage detection using stator current monitoring,” IEEE Trans. Ind. Appl., vol. 31, no. 6, pp. 1274–1279, 1995. C. Peeters, P. Guillaume, and J. Helsen, “Vibration-based bearing fault detection for operations and maintenance cost reduction in wind energy,” Renew. Energy, vol. 116, pp. 74–87, 2018. W. A. Smith and R. B. Randall, “Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study,” Mech. Syst. Signal Process., vol. 64–65, pp. 100–131, 2015. J. Antoni, F. Bonnardot, A. Raad, and M. El Badaoui, “Cyclostationary modelling of rotating machine vibration signals,” Mech. Syst. Signal Process., vol. 18, no. 6, pp. 1285–1314, 2004. E. Mendel et al., “Automatic bearing fault pattern recognition using vibration signal analysis,” IEEE Int. Symp. Ind. Electron., pp. 955–960, 2008. D. Wang, X. Zhao, L. L. Kou, Y. Qin, Y. Zhao, and K. L. Tsui, “A simple and fast guideline for generating enhanced/squared envelope spectra from spectral coherence for bearing fault diagnosis,” Mech. Syst. Signal Process., vol. 122, no. January, pp. 754–768, 2019. O. Janssens et al., “Convolutional Neural Network Based Fault Detection for Rotating Machinery,” J. Sound Vib., vol. 377, pp. 331–345, 2016. W. Zhang, M. P. Jia, L. Zhu, and X. A. Yan, “Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis,” Chinese J. Mech. Eng. (English Ed., vol. 30, no. 4, pp. 782–795, 2017. R. Zhang, H. Tao, L. Wu, and Y. Guan, “Transfer Learning With Neural Networks for Bearing Fault Diagnosis in Changing Working Conditions,” IEEE Access, vol. 5, pp. 14347–14357, 2017. J. Antoni, “The spectral kurtosis: A useful tool for characterising non-stationary signals,” Mech. Syst. Signal Process., vol. 20, no. 2, pp. 282–307, 2006. J. Antoni and R. B. Randall, “The spectral kurtosis: Application to the vibratory surveillance and diagnostics of rotating machines,” Mech. Syst. Signal Process., vol. 20, no. 2, pp. 308–331, 2006. J. Antoni, “Fast computation of the kurtogram for the detection of transient faults,” Mech. Syst. Signal Process., vol. 21, no. 1, pp. 108–124, 2007. R. B. Randall, J. Antoni, and S. Chobsaard, “The relationship between spectral correlation and envelope analysis in the diagnostics of bearing faults and other cyclostationary machine signals,” Mech. Syst. Signal Process., vol. 15, no. 5, pp. 945–962, 2001. A. C. McCormick and A. K. Nandi, “Cyclostationarity in Rotating Machine Vibrations 1 Introduction 2 Wide-sense Cyclostationarity,” Mech. Syst. Signal Process., vol. 12, no. 2, pp. 225–242, 1998. J. Antoni, “Cyclic spectral analysis in practice,” Mech. Syst. Signal Process., vol. 21, no. 2, pp. 597–630, 2007. M. D. Coats and R. B. Randall, “Order-Tracking with and without a tacho signal for gear fault diagnostics,” Aust. Acoust. Soc. Conf. 2012, Acoust. 2012 Acoust. Dev. Environ., no. November, pp. 447–454, 2012. K. R. Fyfe and E. D. S. Munck, “Analysis of computed order tracking,” Mech. Syst. Signal Process., vol. 11, no. 2, pp. 187–205, 1997. S. J. Idehara, A. Luiz, A. Mesquita, U. A. Miranda, M. D. Jr, and D. Ph, “Order tracking methods analysis,” no. 1, 2003. S. Schmidt, P. S. Heyns, and J. P. de Villiers, “A tacholess order tracking methodology based on a probabilistic approach to incorporate angular acceleration information into the maxima tracking process,” Mech. Syst. Signal Process., vol. 100, pp. 630–646, 2018. J. Urbanek, T. Barszcz, and J. Antoni, “A two-step procedure for estimation of instantaneous rotational speed with large fluctuations,” Mech. Syst. Signal Process., vol. 38, no. 1, pp. 96–102, 2013. Q. Leclère, H. André, and J. Antoni, “A multi-order probabilistic approach for Instantaneous Angular Speed tracking debriefing of the CMMNO?14 diagnosis contest,” Mech. Syst. Signal Process., vol. 81, pp. 375–386, 2016. D. Abboud, J. Antoni, S. Sieg-Zieba, and M. Eltabach, “Deterministic-random separation in nonstationary regime,” J. Sound Vib., vol. 362, pp. 305–326, 2016. P. Borghesani, P. Pennacchi, R. B. Randall, N. Sawalhi, and R. Ricci, “Application of cepstrum pre-whitening for the diagnosis of bearing faults under variable speed conditions,” Mech. Syst. Signal Process., vol. 36, no. 2, pp. 370–384, 2013. D. Abboud, S. Baudin, J. Antoni, D. Rémond, M. Eltabach, and O. Sauvage, “The spectral analysis of cyclo-non-stationary signals,” Mech. Syst. Signal Process., 2016. G. D’Elia, Z. Daher, and J. Antoni, “A novel approach for the cyclo-non-stationary analysis of speed varying signals,” Proc. ISMA 2010 - Int. Conf. Noise Vib. Eng. Incl. USD 2010, pp. 2801–2814, 2010. W. A. Smith, R. B. Randall, X. de C. du Mée, and P. Peng, “Use of cyclostationary properties to diagnose planet bearing faults in variable speed conditions,” in 10th {DST} group international conference on health and usage monitoring systems, 17th Australian aerospace congress, 2017, pp. 26–28. J. Berntsen, A. Brandt, and K. Gryllias, “Enhanced demodulation band selection based on Operational Modal Analysis (OMA) for bearing diagnostics,” Mech. Syst. Signal Process., vol. 181, no. July, p. 109300, 2022. A. Mauricio, D. Helm, M. Timusk, J. Antoni, and K. Gryllias, “Novel Cyclo-Nonstationary Indicators for Monitoring of Rotating Machinery Operating Under Speed and Load Varying Conditions,” J. Eng. Gas Turbines Power, vol. 144, no. 4, Apr. 2022. A. Mauricio et al., “Bearing diagnostics under strong electromagnetic interference based on Integrated Spectral Coherence,” Mech. Syst. Signal Process., vol. 140, p. 106673, 2020. M. Nakhaeinejad and M. D. Bryant, “Dynamic modeling of rolling element bearings with surface contact defects using bond graphs,” J. Tribol., vol. 133, no. 1, pp. 1–12, 2011. J. D. Arango and A. Restrepo-martinez, “Rolling Element Bearing Diagnosis by Improved Envelope Spectrum : Optimal Frequency Band Selection,” vol. 15, no. 8, pp. 322–330, 2021. A. Mauricio, W. A. Smith, R. B. Randall, J. Antoni, and K. Gryllias, “Improved Envelope Spectrum via Feature Optimisation-gram (IESFOgram): A novel tool for rolling element bearing diagnostics under non-stationary operating conditions,” Mech. Syst. Signal Process., vol. 144, p. 106891, 2020. F. Cong, J. Chen, G. Dong, and M. Pecht, “Vibration model of rolling element bearings in a rotor-bearing system for fault diagnosis,” J. Sound Vib., vol. 332, no. 8, pp. 2081–2097, 2013. L. R. Kahn, “Single-Sideband Transmission by Envelope Elimination and Restoration,” Proc. IRE, vol. 40, no. 7, pp. 803–806, 1952. C. Mishra, A. K. Samantaray, and G. Chakraborty, “Ball bearing defect models: A study of simulated and experimental fault signatures,” J. Sound Vib., vol. 400, no. July, pp. 86–112, 2017.
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dc.format.extent.spa.fl_str_mv	167 páginas
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Medellín - Minas - Maestría en Ingeniería Mecánica
dc.publisher.faculty.spa.fl_str_mv	Facultad de Minas
dc.publisher.place.spa.fl_str_mv	Medellín, Colombia
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Medellín
institution	Universidad Nacional de Colombia
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spelling	Atribución-NoComercial-CompartirIgual 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Guevara Carazas, Fernando Jesúse547a5abc87fbf8110af5d3714c280f6Restrepo Martínez, Alejandro85baf6fd49c3422762a87334de5fbbc2Arango Castrillón, Juan Davidc95ddcc494375773dff12d7f870c3bb3Gestión, Operación y Mantenimiento de Activos - GomacRestrepo Martínez, Alejandro [0000-0001-8978-2077]Arango Castrillón, Juan David [0000-0002-4615-9186]Guevara Carazas, Fernando Jesús [0000-0001-8529-4383]2023-07-27T16:44:03Z2023-07-27T16:44:03Z2023-01-31https://repositorio.unal.edu.co/handle/unal/84318Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramasRolling Element Bearings –REB are a fundamental part of most of rotating machines. Consequently, their fault detection and timely diagnosis are of great interest to improve the reliability and maintainability of rotational equipment. Vibration analysis is the most widely used tool for bearing diagnostics. Despite advances in algorithms and digital signal processing for diagnosis, it is common to find great advances for cases in which operating conditions remain stationary. This is not the case with many rotating equipment, where the angular velocity can be variable over time. In general, operation under variable speed conditions generates vibratory responses at which angle-periodic phenomena are combined with time-periodic phenomena. This work presents a method for the diagnosis of bearing failures, using "Computer Order Tracking" algorithms, Cycle-Non-Stationarity analysis and Spectral Kurtosis analysis. The method was evaluated using a series of signals captured under variable speed conditions and a series of simulated signals, under different load and speed conditions. Additionally, a signal demodulation strategy is proposed, and a classifier is presented, trained with a combination between simulated signals and captured signals for the diagnosis of types of failures.Los rodamientos, son una parte fundamental de la mayoría de máquinas rotativas, consecuentemente, la detección de fallas y el diagnóstico temprano de estos elementos son de gran interés para incrementar los niveles de confiabilidad y mantenibilidad de los equipos rotativos. El análisis de vibraciones es la herramienta más utilizada para el diagnóstico de rodamientos. Pese a los avances en los algoritmos y procesamiento digital de señales para el diagnóstico, es común encontrar grandes avances para casos en los cuales las condiciones operativas se mantienen estacionarias. Este no es el caso de muchos equipos rotativos, donde la velocidad angular puede resultar variable en el tiempo. En general, la operación bajo condiciones de velocidad variable genera respuestas vibratorias en las cuales se combinan fenómenos ángulo-periódicos con fenómenos tiempo-periódicos. Este trabajo presenta un método para el diagnóstico de fallas en rodamientos, utilizando algoritmos de “Computer Order Tracking”, análisis de Ciclo-No-Estacionareidad y análisis de Kurtosis Espectral. El método fue evaluado utilizando una serie de señales capturadas bajo condiciones de velocidad variable y una serie de señales simuladas, bajo diferentes condiciones de carga y velocidad. Adicionalmente se propone una estrategia para la demodulación de señales, y se presenta un clasificador, entrenado con una combinación entre señales simuladas y señales capturadas para el diagnóstico de tipos de fallas. (Texto tomado de la fuente)MaestríaMagister en Ingeniería MecánicaPredictive MaintenanceÁrea Curricular de Ingeniería Mecánica167 páginasapplication/pdfengUniversidad Nacional de ColombiaMedellín - Minas - Maestría en Ingeniería MecánicaFacultad de MinasMedellín, ColombiaUniversidad Nacional de Colombia - Sede Medellín530 - Física::534 - Sonido y vibraciones relacionadas620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingenieríaIngeniería mecánicaVibración - MedicionesRolling Element BearingVibration AnalysisNon-stationary SpeedCyclo-non-stationary analysisComputer order trackingSpectral KurtosisRodamientoAnálisis de VibracionesVelocidad variableCyclo-No EstacionareidadOrder Tracking ComputarizadoKurtosis EspectralDiagnosis of incipient failures in rolling element bearings under nonstationary operation conditions by vibration analysisDiagnóstico de fallas incipientes en rodamientos bajo condiciones de operación no estacionarias mediante análisis de vibracionesTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMRedColLaReferenciaR. Potter and M. Gribler, “Computed order tracking obsoletes older methods,” SAE Tech. Pap., no. l, pp. 63–67, 1989.R. B. Randall and J. Antoni, “Rolling element bearing diagnostics---A tutorial,” Mech. Syst. Signal Process., vol. 25, no. 2, pp. 485–520, 2011.F. Bonnardot, R. B. Randall, and J. Antoni, “Enhanced unsupervised noise cancellation using angular resampling for planetary bearing fault diagnosis,” Int. J. Acoust. Vib., vol. 9, no. 2, pp. 51–60, 2004.P. D. McFadden, “Interpolation techniques for time domain averaging of gear vibration,” Mech. Syst. Signal Process., vol. 3, no. 1, pp. 87–97, 1989.C. Peeters et al., “Review and comparison of tacholess instantaneous speed estimation methods on experimental vibration data,” Mech. Syst. Signal Process., vol. 129, pp. 407–436, 2019.P. D. McFadden and J. D. Smith, “Model for the vibration produced by a single point defect in a rolling element bearing,” J. Sound Vib., vol. 96, no. 1, pp. 69–82, 1984.I. Howard, “A Review of Rolling Element Bearing Vibration ‘Detection, Diagnosis and Prognosis,’” DSTO-AMRL Report, DSTO-RR-00113, no. October 1994, pp. 35–41, 1994.B. P. Graney and K. Starry, “Rolling element bearing analysis,” Mater. Eval., vol. 70, no. 1, 2012.D. Abboud, M. Elbadaoui, W. A. Smith, and R. B. Randall, “Advanced bearing diagnostics: A comparative study of two powerful approaches,” Mechanical Systems and Signal Processing, vol. 114. pp. 604–627, 2019.D. Abboud, J. Antoni, M. Eltabach, and S. Sieg-Zieba, “Angle\time cyclostationarity for the analysis of rolling element bearing vibrations,” Meas. J. Int. Meas. Confed., vol. 75, pp. 29–39, 2015.N. K. Verma and T. S. S. Subramanian, “Cost benefit analysis of intelligent condition based maintenance of rotating machinery,” 2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA). 2012.B. Al-Najjar and I. Alsyouf, “Enhancing a company’s profitability and competitiveness using integrated vibration-based maintenance: A case study,” Eur. J. Oper. Res., vol. 157, no. 3, pp. 643–657, 2004.B. Al-Najjar, “The lack of maintenance and not maintenance which costs: A model to describe and quantify the impact of vibration-based maintenance on company’s business,” Int. J. Prod. Econ., vol. 107, no. 1, pp. 260–273, May 2007.R. B. Randall, Vibration-based Condition Monitoring: Industrial, Aerospace and Automotive Applications. John Wiley & Sons, 2011.P. D. McFadden and J. D. Smith, “Vibration monitoring of rolling element bearings by the high-frequency resonance technique --- a review,” Tribology International, vol. 17, no. 1. pp. 3–10, 1984.M. J. Dowling, “Application of non-stationary analysis to machinery monitoring,” in 1993 {IEEE} International Conference on Acoustics, Speech, and Signal Processing, 1993, vol. 1, pp. 59--62 vol.1.D. Rémond, J. Antoni, and R. B. Randall, “Editorial for the special issue on Instantaneous Angular Speed ({IAS}) processing and angular applications,” Mechanical Systems and Signal Processing, vol. 44, no. 1–2. pp. 1–4, 2014.{OREDA}: Offshore Reliability Data Handbook. OREDA Participants, 2009.M. El Hachemi Benbouzid, “A review of induction motors signature analysis as a medium for faults detection,” IEEE Trans. Ind. Electron., vol. 47, no. 5, pp. 984–993, 2000.R. R. Schoen, T. G. Habetler, F. Kamran, and R. G. Bartfield, “Motor bearing damage detection using stator current monitoring,” IEEE Trans. Ind. Appl., vol. 31, no. 6, pp. 1274–1279, 1995.C. Peeters, P. Guillaume, and J. Helsen, “Vibration-based bearing fault detection for operations and maintenance cost reduction in wind energy,” Renew. Energy, vol. 116, pp. 74–87, 2018.W. A. Smith and R. B. Randall, “Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study,” Mech. Syst. Signal Process., vol. 64–65, pp. 100–131, 2015.J. Antoni, F. Bonnardot, A. Raad, and M. El Badaoui, “Cyclostationary modelling of rotating machine vibration signals,” Mech. Syst. Signal Process., vol. 18, no. 6, pp. 1285–1314, 2004.E. Mendel et al., “Automatic bearing fault pattern recognition using vibration signal analysis,” IEEE Int. Symp. Ind. Electron., pp. 955–960, 2008.D. Wang, X. Zhao, L. L. Kou, Y. Qin, Y. Zhao, and K. L. Tsui, “A simple and fast guideline for generating enhanced/squared envelope spectra from spectral coherence for bearing fault diagnosis,” Mech. Syst. Signal Process., vol. 122, no. January, pp. 754–768, 2019.O. Janssens et al., “Convolutional Neural Network Based Fault Detection for Rotating Machinery,” J. Sound Vib., vol. 377, pp. 331–345, 2016.W. Zhang, M. P. Jia, L. Zhu, and X. A. Yan, “Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis,” Chinese J. Mech. Eng. 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

Diagnosis of incipient failures in rolling element bearings under nonstationary operation conditions by vibration analysis

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