Use of Edge Computing for Predictive Maintenance of Industrial Electric Motors
Industrial Internet of Things has become a reality in many kind of industries. In this paper, We explore the case of high quantity of raw data generated by a machine. In the aforementioned case is not viable store and process the data in a traditional Internet of Things architecture. For this case,...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2019
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/9170
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/9170
- Palabra clave:
- Edge computing
Industrial internet of things
Predictive maintenance
Electric motors
Internet of things
Maintenance
Architecture-based
Internet of things architectures
Machine monitoring
Potential benefits
Predictive maintenance
Proof of concept
Edge computing
- Rights
- restrictedAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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Use of Edge Computing for Predictive Maintenance of Industrial Electric Motors |
| title |
Use of Edge Computing for Predictive Maintenance of Industrial Electric Motors |
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Use of Edge Computing for Predictive Maintenance of Industrial Electric Motors Edge computing Industrial internet of things Predictive maintenance Electric motors Internet of things Maintenance Architecture-based Internet of things architectures Machine monitoring Potential benefits Predictive maintenance Proof of concept Edge computing |
| title_short |
Use of Edge Computing for Predictive Maintenance of Industrial Electric Motors |
| title_full |
Use of Edge Computing for Predictive Maintenance of Industrial Electric Motors |
| title_fullStr |
Use of Edge Computing for Predictive Maintenance of Industrial Electric Motors |
| title_full_unstemmed |
Use of Edge Computing for Predictive Maintenance of Industrial Electric Motors |
| title_sort |
Use of Edge Computing for Predictive Maintenance of Industrial Electric Motors |
| dc.contributor.editor.none.fl_str_mv |
Figueroa-Garcia J.C. Duarte-Gonzalez M. Jaramillo-Isaza S. Orjuela-Canon A.D. Diaz-Gutierrez Y. |
| dc.subject.keywords.none.fl_str_mv |
Edge computing Industrial internet of things Predictive maintenance Electric motors Internet of things Maintenance Architecture-based Internet of things architectures Machine monitoring Potential benefits Predictive maintenance Proof of concept Edge computing |
| topic |
Edge computing Industrial internet of things Predictive maintenance Electric motors Internet of things Maintenance Architecture-based Internet of things architectures Machine monitoring Potential benefits Predictive maintenance Proof of concept Edge computing |
| description |
Industrial Internet of Things has become a reality in many kind of industries. In this paper, We explore the case of high quantity of raw data generated by a machine. In the aforementioned case is not viable store and process the data in a traditional Internet of Things architecture. For this case, We use an architecture based on edge computing and Industrial Internet of Things concepts and apply them to a case of machine monitoring for predictive maintenance. The proof of concept shows the potential benefits in real industrial applications. © 2019, Springer Nature Switzerland AG. |
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2019 |
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2019 |
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2020-03-26T16:33:07Z |
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2020-03-26T16:33:07Z |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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Communications in Computer and Information Science; Vol. 1052, pp. 523-533 |
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9783030310189 |
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18650929 |
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https://hdl.handle.net/20.500.12585/9170 |
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10.1007/978-3-030-31019-6_44 |
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Universidad Tecnológica de Bolívar |
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Repositorio UTB |
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57212008502 57212005233 55498635300 |
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Communications in Computer and Information Science; Vol. 1052, pp. 523-533 9783030310189 18650929 10.1007/978-3-030-31019-6_44 Universidad Tecnológica de Bolívar Repositorio UTB 57212008502 57212005233 55498635300 |
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https://hdl.handle.net/20.500.12585/9170 |
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eng |
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eng |
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16 October 2019 through 18 October 2019 |
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Springer |
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Springer |
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6th Workshop on Engineering Applications, WEA 2019 |
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Figueroa-Garcia J.C.Duarte-Gonzalez M.Jaramillo-Isaza S.Orjuela-Canon A.D.Diaz-Gutierrez Y.De Leon V.Alcazar Y.Villa Ramírez, José Luis2020-03-26T16:33:07Z2020-03-26T16:33:07Z2019Communications in Computer and Information Science; Vol. 1052, pp. 523-533978303031018918650929https://hdl.handle.net/20.500.12585/917010.1007/978-3-030-31019-6_44Universidad Tecnológica de BolívarRepositorio UTB572120085025721200523355498635300Industrial Internet of Things has become a reality in many kind of industries. In this paper, We explore the case of high quantity of raw data generated by a machine. In the aforementioned case is not viable store and process the data in a traditional Internet of Things architecture. For this case, We use an architecture based on edge computing and Industrial Internet of Things concepts and apply them to a case of machine monitoring for predictive maintenance. The proof of concept shows the potential benefits in real industrial applications. © 2019, Springer Nature Switzerland AG.Department of Science, Information Technology and Innovation, Queensland Government, DSITI Ministry of Information and Communications Technology, Iran Departamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS), COLCIENCIAS Fondo Nacional de Ciencia Tecnología e Innovación, FONACIT: FP44842-502-2015The authors would like acknowledge the cooperation of all partners within the Centro de Excelencia y Apropiaci?n en Internet de las Cosas (CEA-IoT) project. The authors would also like to thank all the institutions that supported this work: the Colombian Ministry for the Information and Communications Technology (Ministerio de Tecnolog?as de la Informaci?n y las Comunicaciones-MinTIC ) and the Colombian Administrative Department of Science, Technology and Innovation (Departamento Administrativo de Ciencia, Tecnolog?a e Innovaci?n-Colcien-cias) through the Fondo Nacional de Financiamiento para la Ciencia, la Tecnolog?a y la Innovaci?n Francisco Jos? de Caldas (Project ID: FP44842-502-2015).Recurso electrónicoapplication/pdfengSpringerhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/restrictedAccessAtribución-NoComercial 4.0 Internacionalhttp://purl.org/coar/access_right/c_16echttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85075644036&doi=10.1007%2f978-3-030-31019-6_44&partnerID=40&md5=a3ce01c10e2bb04764c4bb875b31115f6th Workshop on Engineering Applications, WEA 2019Use of Edge Computing for Predictive Maintenance of Industrial Electric Motorsinfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionConferenciahttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_c94fEdge computingIndustrial internet of thingsPredictive maintenanceElectric motorsInternet of thingsMaintenanceArchitecture-basedInternet of things architecturesMachine monitoringPotential benefitsPredictive maintenanceProof of conceptEdge computing16 October 2019 through 18 October 2019Gregori, F., Papetti, A., Pandolfi, M., Peruzzini, M., Germani, M., Improving a production site from a social point of view: An IoT infrastructure to monitor workers condition (2018) Procedia CIRP, 72, pp. 886-891. , https://doi.org/10.1016/j.procir.2018.03.057.http://www.sciencedirect.com/science/article/pii/S2212827118301598.ISSN2212-8271(2016) White Paper of Edge Computing ConsortiumBoyes, H., Hallaq, B., Cunningham, J., Watson, T., The industrial Internet of Things (IIoT): An analysis framework (2018) Comput. Ind., 101, pp. 1-12. , http://www.sciencedirect.com/science/article/pii/S0166361517307285.ISSN0166-3615Civerchia, F., Bocchino, S., Salvadori, C., Rossi, E., Maggiani, L., Petracca, M., Industrial Internet of Things monitoring solution for advanced predictive maintenance applications (2017) J. Ind. Inf. Integr., 7, pp. 4-12. , http://www.sciencedirect.com/science/article/pii/S2452414X16300954.ISSN2452-414XCao, J., Zhang, Q., Li, Y., Shi, W., Xu, L., Edge computing: Vision and challenges (2016) IEEE Iot J, 3, pp. 637-646Industrial Internet Consortium. Introduction to edge computing in IIoT. An Industrial Internet Consortium White Paper, IIC:WHT:IN24:V1.0:PB:20180618. Edge Computing Task GroupSchmidt, B., Wang, L., Galar, D., Semantic framework for predictive maintenance in a cloud environment (2017) Procedia CIRP, 62, pp. 583-588. , https://doi.org/10.1016/j.procir.2016.06.047Taherizadeh, S., Jones, A.C., Taylor, I., Zhao, Z., Stankovski, V., Monitoring self-adaptive applications within edge computing frameworks: A state-of-the-art review (2018) J. Syst. Softw., 136, pp. 19-38. , Suppl. CFujishima, M., Mori, M., Nishimura, K., Takayama, M., Kato, Y., Development of sensing interface for preventive maintenance of machine tools (2017) Procedia CIRP, 61, pp. 796-799. , http://www.sciencedirect.com/science/article/pii/S2212827116313749, ISSN 2212-8271Cruz, A.M.E., (2013) ESTUDIO DE UN SISTEMA DE MANTENIMIENTO PREDIC-TIVO BASADO EN ANÁLISIS DE VIBRACIONES IMPLANTADO EN INSTA-LACIONES DE BOMBEO Y GENERACIÓN https://power-mi.com/es/content/power-mi-lanza-manual-de-anPease, S.G., Conway, P.P., West, A.A., Hybrid ToF and RSSI real-time semantic tracking with an adaptive industrial internet of things architecture (2017) J. Netw. Comput. Appl., 99, pp. 98-109Flores, R., Asiaín, T.I., Diagnóstico de Fallas en Máquinas Eléctricas Rota-torias Utilizando la Técnica de Espectros de Frecuencia de Bandas Laterales (2011) Información Tecnológica, 22 (4), pp. 73-84. , https://doi.org/10.4067/S0718-07642011000400009Talbot, C.E., Saavedra, P.N., Valenzuela, M.A., Diagnóstico de la Condición de las Barras de Motores de Inducción (2013) Información tecnológica, 24 (4), pp. 85-94. , https://doi.org/10.4067/S0718-07642013000400010Lin, S.-W., (2017) Architecture Alignment and InteroperabilityMourtzis, D., Gargallis, A., Zogopoulos, V., Modelling of customer oriented applications in product lifecycle using RAMI 4.0 Procedia Manuf., 28, pp. 31-36. , http://www.sciencedirect.com/science/article/pii/S2351978918313489Lin, S.W., Industrial internet reference architecture (2015) Technical Report, Industrial Internet Consortium (IIC)Packard, H., (2017) Real-Time Analysis and Condition Monitoring with Predictive Maintenance. Transforming Data into Value with HPE EdgelineGierej, S., The framework of business model in the context of industrial Internet of Things (2017) Procedia Eng., 182, pp. 206-212. , http://www.sciencedirect.com/science/article/pii/S1877705817313024, ISSN 1877-7058Shi, W., Cao, J., Zhang, Q., Li, Y., Xu, L., Edge computing: Vision and challenges (2016) IEEE Iot J, 3 (5), pp. 637-646Barroso, M., Dolores, M., (2019) Edge Computing Para IotBossio, G., de Angelo, C., García, G., (2006) Técnicas De Mantenimiento Predictivo En Máquinas Eléctricas: Diagnóstico De Fallas En El Rotor De Los Motores De Inducción. Megavatios, pp. 194-208. , ppBellini, A., On-field experience with online diagnosis of large induction motors cage failures using MCSA (2002) IEEE Trans. Ind. Appl., 38 (4), pp. 1045-1053. , https://doi.org/10.1109/TIA.2002.800591http://purl.org/coar/resource_type/c_c94fTHUMBNAILMiniProdInv.pngMiniProdInv.pngimage/png23941https://repositorio.utb.edu.co/bitstream/20.500.12585/9170/1/MiniProdInv.png0cb0f101a8d16897fb46fc914d3d7043MD5120.500.12585/9170oai:repositorio.utb.edu.co:20.500.12585/91702023-04-21 15:43:47.402Repositorio Institucional UTBrepositorioutb@utb.edu.co |