Aplicación de un modelo estocástico para el Análisis RAM de Máquinas Rotatorias en la Industria 4.0

Palabras clave: ARIMA, confiabilidad, disponibilidad, mantenibilidad, ventilador industrial

Resumen

La aplicación de conceptos de la Industria 4.0 promovidas a través del mantenimiento predictivo de un activo industrial, marca la forma de la gestión operativa de una fábrica a largo plazo. El análisis de la data histórica de los activos brinda la oportunidad de aplicar técnicas como el modelamiento de datos, que definen el comportamiento de las máquinas a través del tiempo. Este paper presenta un análisis de confiabilidad, disponibilidad y mantenibilidad (RAM) de un grupo de ventiladores industriales que forman parte de un proceso de fabricación de clinker, desde una perspectiva que relaciona datos históricos de vibración con los estados que toman las máquinas, clasificados según el estándar ISO 14694. Para ello, se caracterizan series temporales por cada ventilador, y se obtienen métricas descriptivas, que habilitan la aplicación de un tipo de modelo autorregresivo integrado de media móvil, para predecir las condiciones que tomarán los equipos en los siguientes doce meses asociándolas al cálculo de los indicadores RAM, que definen la toma de decisiones en el marco operativo de la planta.

Descargas

La descarga de datos todavía no está disponible.

Citas

Achouch M., Dimitrova M., Ziane K., Karganroudi SS., Dhouib R., Ibrahim H., Adda M. (2022). On Predictive Maintenance in Industry 4.0: Overview, Models, and Challenges. Applied Sciences-Basel, Volume12, Issue16, Article Number8081, Aug 2022. DOI10.3390/app12168081

Alves F., Badikyan H., Moreira A., Azevedo J., Moreira P.M., Romero L., Leitao P. (2020). Deployment of a Smart and Predictive Maintenance System in an Industrial Case Study. IEEE International Symposium on Industrial Electronics, vol. 2020-June, pp. 493–498. Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ISIE45063.2020.9152441

Aly, M. F., Afefy, I. H., Abdel-Magied, R. K., Elhalim, E. K. (2018). A comprehensive model of reliability, availability, and maintainability (RAM) for industrial systems evaluations. JJMIE, 12(1), 59-67

Alya M. F., Afefya I. H., Abdel-Magiedb R. K., Abd Elhalimc E. K. (2018). A Comprehensive Model of Reliability, Availability, and Maintainability (RAM) for Industrial Systems Evaluations. Jordan Journal of Mechanical and Industrial Engineering, Volume 12 Number 1, June. 2018, ISSN 1995-6665, Pages 59 - 67

Barbu V.S., Vergne N. (2019). Reliability and Survival Analysis for Drifting Markov Models: Modeling and Estimation. Methodol Comput. Appl Probab 21, 1407–1429. https://doi.org/10.1007/s11009-018-9682-8

Bousdekis A., Apostolou D., Mentzas G. (2019). Predictive Maintenance in the 4th Industrial Revolution: Benefits, Business Opportunities and Managerial Implications. IEEE Engineering Management Review. DOI 10.1109/EMR.2019.2958037

Bousdekis A., Apostolou D., Mentzas G. (2020). Predictive Maintenance in the 4th Industrial Revolution: Benefits, Business Opportunities, and Managerial Implications. IEEE Engineering Management Review, vol. 48, no. 1, pp. 57-62, 1 Firstquarter. doi: 10.1109/EMR.2019.2958037

Cachada A. et al. (2018). Maintenance 4.0: Intelligent and Predictive Maintenance System Architecture. 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA), Turin, Italy, 2018, pp. 139-146, doi: 10.1109/ETFA.2018.8502489.

Cebreiro N., Rodríguez (2021). Principios básicos de las cadenas de Markov. http://hdl.handle.net/10347/28790.2021

Chen B., Liu Y., Zhang C., Wang Z. (2020). Time Series Data for Equipment Reliability Analysis With Deep Learning. IEEE Access, 8, 105484–105493, May 2020. doi:10.1109/access.2020.3000006

Dindarloo S. (2015). Reliability Forecasting of a Load-Haul-Dump Machine: A Comparative Study of ARIMA and Neural Networks. Wiley Online Library July 2015. DOI: 10.1002/qre.1844

Gámiz M.L., Limnios N., Segovia-García M.d.C. (2023). Hidden markov models in reliability and maintenance. European Journal of Operational Research, Volume 304, Issue 3, Pages 1242-1255, ISSN 0377-2217. https://doi.org/10.1016/j.ejor.2022.05.006

Gonçalves S., Fruett F., Dalfré Filho G., Giesbrecht M. (2021). Faults detection and classification in a centrifugal pump from vibration data using markov parameters. Mechanical Systems and Signal Processing, Volume 158, 2021, 107694, ISSN 0888-3270. https://doi.org/10.1016/j.ymssp.2021.107694

Hyndman R.J., Athanasopoulos G., (2021). Forecasting: principles and practice, 3rd edition, OTexts: Melbourne, Australia. OTexts.com/fpp3

Jagtap H.P., Bewoor A.K., Kumar R. (2020). RAM analysis and availability optimization of thermal power plant water circulation system using PSO. Energy Reports. https://doi.org/10.1016/j.egyr.2020.12.025

Ji W., Abou Rizk S.M. (2018). Data-Driven Simulation Model for Quality-Induced Rework Cost Estimation and Control Using Absorbing Markov Chains. Journal of Construction Engineering and Management", 144(8), 04018078. doi:10.1061/(asce)co.1943-7862.0001534

Jimenez-Cortadi A., Irigoien I., Boto F., Sierra B., Rodriguez G. (2020). Predictive Maintenance on the Machining Process and Machine Tool. Applied Sciences-Basel, Volume10, Issue1, Article Number224, JAN 2020. DOI10.3390/app10010224

Komal, Sharmaa S.P., Dinesh Kumar. (2010). RAM analysis of repairable industrial systems utilizing uncertain data. Applied Soft Computing 10 (2010) 1208–1221 doi:10.1016/j.asoc.2009.12.019

Manco G., Ritacco E., Rullo P., Gallucci L., Astill W., Kimber D., Antonelli M. (2017). Fault Detection and Explanation through Big Data Analysis on Sensor Streams, Expert Systems With Applications. doi: 10.1016/j.eswa.2017.05.079

Mishra S., Bordin C., Taharaguchi K., Purkayastha A. (2022). Predictive analytics beyond time series: Predicting series of events extracted from time series data. Wind Energy, Volume25, Issue9, Page1596-1609, SEP 2022. DOI: 10.1002/we.2760

Naderkhani F., Jafari L., Makis V. (2017) Optimal CBM policy with two sampling intervals. Journal of Quality in Maintenance Engineering, Volume23, Issue1, Page95-112, March 2017. DOI10.1108/JQME-07-2015-0030

Quezada A., Rodríguez L., Pérez J., Rodríguez I. (2018). Stochastic processes applied in degradation data analysis - literature review. Mundo FESC, ISSN-e 2216-0388, ISSN 2216-0353, Vol. 8, Nº. 16, 2018, págs. 85-94

R Core Team (2022). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Vienna, Austria. https://www.R-project.org/

Reis A.S. T., Campos F.C. (2021). Industry 4.0 influences on maintenance operation: a bibliometric analysis. Conference on Emerging Technologies and Factory Automation (ETFA ), Volume 53, Issue 2, pp. 01-08. doi:10.1109/ETFA45728.2021.9613499

Ruiz-Sarmiento J., Monroy J., Moreno F., Galindo C., Bonelo J., Gonzalez-Jimenez J. (2020). A predictive model for the maintenance of industrial machinery in the context of industry 4.0. Eng. Appl. Artif. Intell. 87, C. https://doi.org/10.1016/j.engappai.2019.103289

Saini M., Kumar A., Sinwar D. (2022). Parameter estimation, reliability and maintainability analysis of sugar manufacturing plant. Int J Syst Assur Eng Manag 13, 231–249. February 2022. https://doi.org/10.1007/s13198-021-01216-6

Sezer E., Romero D., Guedea F., Macchi M., Emmanouilidis C. (2018). An Industry 4.0-enabled Low Cost Predictive Maintenance Approach for SMEs: A Use Case Applied to a CNC Turning Centre. 2018 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), 2018, 978-1-5386-1469-3/18

Spendla L., Kebisek M., Tanuska P., Hrcka L. (2017). Concept of predictive maintenance of production systems in accordance with industry 4.0. IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI), pp. 000405-000410, doi: 10.1109/SAMI.2017.7880343

Stavropoulos Ch.N., Fassois S.D. (2000). Non-stationary functional series modeling and analysis of hardware reliability series: a comparative study using rail vehicle interfailure times. Reliability Engineering and System Safety 68 (2000) 169–183

Teoh Y. K., Gill S. S., Parlikad A. K. (2021). IoT and Fog Computing based Predictive Maintenance Model for Effective Asset Management in Industry 4.0 using Machine Learning. IEEE Internet of Things Journal. doi: 10.1109/JIOT.2021.3050441.

Topic D., Sljivac D., Stojkov M. (2016). Reliability model of different wind power plant configuration using sequential Monte Carlo simulation. Eksploatacja I Niezawodnosc-Maintenance and Reliability, Volume18, Issue2, pp 237-244. DOI10.17531/ein.2016.2.11

Tsarouhas, P. (2019). Statistical analysis of failure data for estimating reliability, availability and maintainability of an automated croissant production line. Journal of Quality in Maintenance Engineering, 25(3), 452-475.

Villanustre F. (2015). Industrial Big Data Analytics: Lessons from the Trenches. IEEE/ACM 1st International Workshop on Big Data Software Engineering, pp. 1-3. doi: 10.1109/BIGDSE.2015.8

Voigt T. et al., (2021). Advanced Data Analytics Platform for Manufacturing Companies. 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA ), Vasteras, Sweden, 2021, pp. 01-08, doi: 10.1109/ETFA45728.2021.9613499.

Yañez Medina M., Gómez de la Vega H., Valbuena Chourio G. (2004).Ingeniería de Confiabilidad y Análisis Probabilístico de Riesgo. Reliability and Risk Management, S. A., ISBN: 980-12-12-0116-9.

Publicado
2023-05-05
Cómo citar
Zambrano , T., & Ponsot, E. (2023). Aplicación de un modelo estocástico para el Análisis RAM de Máquinas Rotatorias en la Industria 4.0 . Ciencia Latina Revista Científica Multidisciplinar, 7(2), 5101-5134. https://doi.org/10.37811/cl_rcm.v7i2.5709
Sección
Artículos