A novel model-based Cauchy-Schwarz divergence condition indicator for gears monitoring during fluctuating speed conditions

Gear monitoring and fault diagnosis are vital for preventing accidents and minimizing economic losses in transportation and industrial systems. Traditional methods use vibration sensors and a two-stage analysis approach: preprocessing data to remove noise and extract relevant components, and generat...

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Detalles Bibliográficos
Autores: Puerto Santana, Cristian|||0000-0002-7380-5198, Ocampo-Martínez, Carlos|||0000-0001-9251-6044, Díaz Rozo, Javier
Tipo de recurso: artículo
Fecha de publicación:2024
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/418285
Acceso en línea:https://hdl.handle.net/2117/418285
https://dx.doi.org/10.1016/j.jsv.2024.118610
Access Level:acceso embargado
Palabra clave:Gear monitoring
Signal processing
Condition indicators
System identification
Cauchy-Schwarz divergence
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
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spelling A novel model-based Cauchy-Schwarz divergence condition indicator for gears monitoring during fluctuating speed conditionsPuerto Santana, Cristian|||0000-0002-7380-5198Ocampo-Martínez, Carlos|||0000-0001-9251-6044Díaz Rozo, JavierGear monitoringSignal processingCondition indicatorsSystem identificationCauchy-Schwarz divergenceÀrees temàtiques de la UPC::Informàtica::Automàtica i controlGear monitoring and fault diagnosis are vital for preventing accidents and minimizing economic losses in transportation and industrial systems. Traditional methods use vibration sensors and a two-stage analysis approach: preprocessing data to remove noise and extract relevant components, and generating a condition indicator to detect behavioral anomalies in the gears over time. Time synchronous averaging is a notable tool for monitoring gears at constant speeds. Such a tool filters sensor signals and extracts rotation-related components by using statistical measurements as condition indicators. However, it has limitations in scenarios with time-varying sampling rates and fluctuating speeds, where statistical measures may not fully capture changes in system parameters. This article proposes a novel methodology for monitoring gears in multivariate rotordynamical systems under fluctuating speed conditions. The method integrates time synchronous averaging, system identification algorithms, and statistical tools. It generates a time-synchronous average signal considering speed fluctuations, computes a state–space model of gear behavior in healthy states, extracts residual data from a data-driven model, and generates a condition indicator based on the Cauchy–Schwarz divergence. The proposed methodology was evaluated using experimental data from three rotor dynamical setups under different operational conditions. Validation showed its effectiveness, especially under high-load conditions with significant speed fluctuations.This work has been supported by the Doctorats Industrials program from the Catalonia Government (Ref. 2022 DI 00093) and the technology transfer project TOFMAN-2 (Ref. C-12446-UPC). Also, it was partially supported by the Spanish Ministry of Science and Innovation through the RTC2019-006871-7 (DSTREAMS) and PID2020-115905RB-C21 (L-BEST) projects.Peer Reviewed20242024-07-1020242024-11-1920262026-12-10journal articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/418285https://dx.doi.org/10.1016/j.jsv.2024.118610reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengembargoed accesshttp://purl.org/coar/access_right/c_f1cfAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/embargoedAccessoai:upcommons.upc.edu:2117/4182852026-05-27T15:37:01Z
dc.title.none.fl_str_mv A novel model-based Cauchy-Schwarz divergence condition indicator for gears monitoring during fluctuating speed conditions
title A novel model-based Cauchy-Schwarz divergence condition indicator for gears monitoring during fluctuating speed conditions
spellingShingle A novel model-based Cauchy-Schwarz divergence condition indicator for gears monitoring during fluctuating speed conditions
Puerto Santana, Cristian|||0000-0002-7380-5198
Gear monitoring
Signal processing
Condition indicators
System identification
Cauchy-Schwarz divergence
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
title_short A novel model-based Cauchy-Schwarz divergence condition indicator for gears monitoring during fluctuating speed conditions
title_full A novel model-based Cauchy-Schwarz divergence condition indicator for gears monitoring during fluctuating speed conditions
title_fullStr A novel model-based Cauchy-Schwarz divergence condition indicator for gears monitoring during fluctuating speed conditions
title_full_unstemmed A novel model-based Cauchy-Schwarz divergence condition indicator for gears monitoring during fluctuating speed conditions
title_sort A novel model-based Cauchy-Schwarz divergence condition indicator for gears monitoring during fluctuating speed conditions
dc.creator.none.fl_str_mv Puerto Santana, Cristian|||0000-0002-7380-5198
Ocampo-Martínez, Carlos|||0000-0001-9251-6044
Díaz Rozo, Javier
author Puerto Santana, Cristian|||0000-0002-7380-5198
author_facet Puerto Santana, Cristian|||0000-0002-7380-5198
Ocampo-Martínez, Carlos|||0000-0001-9251-6044
Díaz Rozo, Javier
author_role author
author2 Ocampo-Martínez, Carlos|||0000-0001-9251-6044
Díaz Rozo, Javier
author2_role author
author
dc.subject.none.fl_str_mv Gear monitoring
Signal processing
Condition indicators
System identification
Cauchy-Schwarz divergence
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
topic Gear monitoring
Signal processing
Condition indicators
System identification
Cauchy-Schwarz divergence
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
description Gear monitoring and fault diagnosis are vital for preventing accidents and minimizing economic losses in transportation and industrial systems. Traditional methods use vibration sensors and a two-stage analysis approach: preprocessing data to remove noise and extract relevant components, and generating a condition indicator to detect behavioral anomalies in the gears over time. Time synchronous averaging is a notable tool for monitoring gears at constant speeds. Such a tool filters sensor signals and extracts rotation-related components by using statistical measurements as condition indicators. However, it has limitations in scenarios with time-varying sampling rates and fluctuating speeds, where statistical measures may not fully capture changes in system parameters. This article proposes a novel methodology for monitoring gears in multivariate rotordynamical systems under fluctuating speed conditions. The method integrates time synchronous averaging, system identification algorithms, and statistical tools. It generates a time-synchronous average signal considering speed fluctuations, computes a state–space model of gear behavior in healthy states, extracts residual data from a data-driven model, and generates a condition indicator based on the Cauchy–Schwarz divergence. The proposed methodology was evaluated using experimental data from three rotor dynamical setups under different operational conditions. Validation showed its effectiveness, especially under high-load conditions with significant speed fluctuations.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-07-10
2024
2024-11-19
2026
2026-12-10
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/418285
https://dx.doi.org/10.1016/j.jsv.2024.118610
url https://hdl.handle.net/2117/418285
https://dx.doi.org/10.1016/j.jsv.2024.118610
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv embargoed access
http://purl.org/coar/access_right/c_f1cf
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/embargoedAccess
rights_invalid_str_mv embargoed access
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Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv embargoedAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
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