Mathematical modeling of optimal maintenance inspection frequencies for parallel lathes as a function of operational context
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Publicación 4. Vol 6. Num 3.2 (Español (España))
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Aug 22, 2023
Keywords:
Optimization, frequencies, maintenance, logbook, model, autoregressive, forecasting.
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Abstract
The optimization of maintenance frequencies using the prediction of failure occurrence resulting from mathematical modeling and in particular through the use of Autoregressive Integrated Moving Average Models (ARIMA) is a topic that has been investigated and developed in recent years, because the results obtained reflect the increase of the different productivity indexes of the intervened machines and equipment, that is, the efficiency and effectiveness of these models in the estimation of these frequencies has been proven. It has been applied a methodology that starts from the generation of a time series based on the Times of Good Operation (TTF) that are recorded in the maintenance logs of the parallel lathe TR - 01, this series is mathematically modeled with the objective of generating an adequate forecast of the appearance of new failures, this allowed to reduce key performance indicators at industrial level as the Average Time of Repair and Maintenance Costs up to 35%, also the repeatability and reproducibility of the proposed methodology makes that the study can be implemented in any physical asset.
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López Telenchana, L. S., Ramos Araujo, C. E., Pérez Londo, N. A., & Moyón Moyón, C. del R. (2023). Mathematical modeling of optimal maintenance inspection frequencies for parallel lathes as a function of operational context. ConcienciaDigital, 6(3.2), 77-96. https://doi.org/10.33262/concienciadigital.v6i3.2.2667
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References
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Çankaya, M. N., & Korbel, J. (2018). Least informative distributions in maximum q-log-likelihood estimation. Physica A: Statistical Mechanics and Its Applications, 509, 140–150. doi: 10.1016/j.physa.2018.06.004
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Mazón Fierro, G. J., & Buñay Guisñan, P. A. (2022). Análisis exploratorio entre modelos matemáticos predictivos, aplicados a la producción de energía mediante series temporales. ConcienciaDigital, 5(3.1),57-78. https://doi.org/10.33262/concienciadigital.v5i3.1.2223.
Melo, J., & Santana, G. (2016). Minado de series de tiempo utilizando la metodología ARIMA. Revista de Investigación y Desarrollo, 2-5: 21-31. ISSN-2444-4987. https://www.ecorfan.org/spain/researchjournals/Investigacion_y_Desarrollo/vol2num5/Revista_de_Investigaci%C3%B3n_y_Desarrollo_V2_N5_3.pdf
Montero, J., Schwartz, S., Vingerhoeds, R., Grabot, B., & Salaün, M. (2020). Towards multi-model approaches to predictive maintenance: A systematic literature survey on diagnostics and prognostics. Journal of Manufacturing Systems, 56, 539–557. https://doi:10.1016/j.jmsy.2020.07.008.
Parreño, F., Parreño C., & Alvarez P. (2023). A matheuristic algorithm for the maintenance planning problem at an electricity transmission system operator, Expert Systems with Applications, 230, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2023.120583.
Pinciroli, L., Baraldi, P., & Zio, E. (2023) Maintenance optimization in industry 4.0, Reliability Engineering & System Safety, 234, ISSN 0951-https://doi.org/10.1016/j.ress.2023.109204.
Pindyck, R. S., & Rubinfeld, D. L. (1998). Econometric models and economic forecasts. (No Title). https://cir.nii.ac.jp/crid/1130000795059255552
Rodas, L., & Castrillón, O. (2019). Predicción de Fallos Mecánicos en Equipos de Envoltura. Información tecnológica, 30(6), 111-122. https://dx.doi.org/10.4067/S0718-07642019000600111
Rodó, P. (2019, julio 30). Contraste de Dickey-Fuller. Economipedia.com
Schwarz, G. (1978). Estimating the dimension of a model. The annals of statistics, 461-464. https://www.jstor.org/stable/2958889
Taneja, K., Ahmad, S., Ahmad, K., & Attri, S. (2016). Time series analysis of aerosol optical depth over New Delhi using Box–Jenkins ARIMA modeling approach, Atmospheric Pollution Research, 7(4), 585-596, ISSN 1309-1042, https://doi.org/10.1016/j.apr.2016.02.004.
Vanderschueren, T., Boute, R., Verdonck, T., Baesens, B., & Verbeke, W. (2023). Optimizing the preventive maintenance frequency with causal machine learning, International Journal of Production Economics, 258, ISSN 0925-5273, https://doi.org/10.1016/j.ijpe.2023.108798.
Walls, L., & Bendell, A., (1987). Time series methods in reliability, Reliability Engineering, Volume 18, Issue 4, 1987, Pages 239-265, ISSN 0143-8174, https://doi.org/10.1016/0143-8174(87)90030-8.
Zahedi-Hosseini, F. (2018). Modeling and simulation for the joint maintenance-inventory optimization of production systems. Winter Simulation Conference (WSC). 3264-3274. https://doi:10.1109/wsc.2018.8632283.
Zdenek, V. & Rudolf, H. (2003). Preventive maintenance optimization on the basis of operating data analysis. Annual Reliability and Maintainability Symposium, 400-406. https://doi:10.1109/rams.2003.1182022.
Zhang, Z., Tang, Q., & Chica, M. (2021). Maintenance costs and makespan minimization for assembly permutation flow shop scheduling by considering preventive and corrective maintenance. Journal of Manufacturing Systems, 59, 549 -564. https://doi:10.1016/j.jmsy.2021.03.020
Adhikari, R., & Agrawal, R. (2013). Hybridization of artificial neural network and Particle Swarm Optimization methods for time series forecasting. International Journal of Applied Evolutionary Computation (IJAEC), 4(3), 75-90. https://www.igi-global.com/article/hybridization-of-artificial-neural-network-and-particle-swarm-optimization-methods-for-time-series-forecasting/95960
Athanasopoulos, G., Hyndman, R., Kourentzes, N., & Petropoulos, F. (2017). Forecasting with temporal hierarchies, European Journal of Operational Research, Volume 262, Issue 1, 2017, Pages 60-74, ISSN 0377-2217, https://doi.org/10.1016/j.ejor.2017.02.046.
Ayeleru, O.O., Fajimi, L.I., Oboirien, B.O., & Olubambi, P.A., (2021). Forecasting municipal solid waste quantity using artificial neural network and supported vector machine techniques: a case study of Johannesburg, South Africa. J. Clean. Prod. 289, 125671. https://doi.org/10.1016/j.jclepro.2020.125671
Baptista, M., Sankararaman, S., de Medeiros, I. P., Nascimento, C., Prendinger, H., & Henriques, E. M. P. (2018). Forecasting fault events for predictive maintenance using data-driven techniques and ARMA modeling. Computers & Industrial Engineering, 115, 41–53. https://doi:10.1016/j.cie.2017.10.033
Box, G.E.P., Jenkins, G.M., & Reinsel, G.C., 1994. Time Series Analysis -Forecasting and Control, third ed. Prentice-Hall, Englewood Cliffs, NJ.
Burnham, K., & Anderson, D. (2002). Selección de modelos e inferencia multimodelo: un enfoque práctico de la teoría de la información. 2ª ed. Nueva York, Springer-Verlag.
Çankaya, M. N., & Korbel, J. (2018). Least informative distributions in maximum q-log-likelihood estimation. Physica A: Statistical Mechanics and Its Applications, 509, 140–150. doi: 10.1016/j.physa.2018.06.004
Cleveland, R., Cleveland, W., McRae, J., & Terpenning, I (1990). “STL: A Seasonal-Trend Decomposition.” Journal of Official Statistics 6 (1): 3–73. https://www.wessa.net/download/stl.pdf
Dickey, D. A., & Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica: journal of the Econometric Society, 1057-1072. https://www.jstor.org/stable/1912517
Fuller, W. A. (1996). Introduction to Statistical Time Series, second ed., New York: John Wiley and Sons.
Hernández-Sampieri, Roberto. (2018). Metodología de la investigación: Las rutas cuantitativa y cualitativa y mixta. México: Mc Graw Hill- Educación.
Ho, S., & Xie, M., (1998). The use of ARIMA models for reliability forecasting and analysis, Computers & Industrial Engineering, 35(1–2), 213-216, ISSN 0360-8352,https://doi.org/10.1016/S0360-8352(98)00066-7. (https://www.sciencedirect.com/science/article/pii/S0360835298000667)
Ho, S., Xie, M., & Goh, T., (2002) A comparative study of neural network and Box-Jenkins ARIMA modeling in time series prediction, Computers & Industrial Engineering, 42 (2–4), 371-375, ISSN 0360-8352, https://doi.org/10.1016/S0360-8352(02)00036-0. http://www.scielo.org.bo/scielo.php?script=sci_arttext&pid=S2409-16182019000100006&lng=es&tlng=es.
Hyndman, R., & Khandakar, Y. (2008). Automatic Time Series Forecasting: The forecast Package for R. Journal of Statistical Software, 27(3), 1–22. https://doi.org/10.18637/jss.v027.i03
Jimenez-Cortadi, A., Irigoien, I., Boto, F., Sierra, B., & Rodríguez, G. (2019). Predictive Maintenance on the Machining Process and Machine Tool. Applied Sciences, 10(1), 224. http// doi:10.3390/app10010224
Laurente, L., & Laurente, F. (2019). Aplicación del modelo ARIMA para la producción de la papa en la región de Puno-Perú. Revista de Investigación e Innovación Agropecuaria y de Recursos Naturales, 6(1), 30-40.
Mazón Fierro, G. J., & Buñay Guisñan, P. A. (2022). Análisis exploratorio entre modelos matemáticos predictivos, aplicados a la producción de energía mediante series temporales. ConcienciaDigital, 5(3.1),57-78. https://doi.org/10.33262/concienciadigital.v5i3.1.2223.
Melo, J., & Santana, G. (2016). Minado de series de tiempo utilizando la metodología ARIMA. Revista de Investigación y Desarrollo, 2-5: 21-31. ISSN-2444-4987. https://www.ecorfan.org/spain/researchjournals/Investigacion_y_Desarrollo/vol2num5/Revista_de_Investigaci%C3%B3n_y_Desarrollo_V2_N5_3.pdf
Montero, J., Schwartz, S., Vingerhoeds, R., Grabot, B., & Salaün, M. (2020). Towards multi-model approaches to predictive maintenance: A systematic literature survey on diagnostics and prognostics. Journal of Manufacturing Systems, 56, 539–557. https://doi:10.1016/j.jmsy.2020.07.008.
Parreño, F., Parreño C., & Alvarez P. (2023). A matheuristic algorithm for the maintenance planning problem at an electricity transmission system operator, Expert Systems with Applications, 230, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2023.120583.
Pinciroli, L., Baraldi, P., & Zio, E. (2023) Maintenance optimization in industry 4.0, Reliability Engineering & System Safety, 234, ISSN 0951-https://doi.org/10.1016/j.ress.2023.109204.
Pindyck, R. S., & Rubinfeld, D. L. (1998). Econometric models and economic forecasts. (No Title). https://cir.nii.ac.jp/crid/1130000795059255552
Rodas, L., & Castrillón, O. (2019). Predicción de Fallos Mecánicos en Equipos de Envoltura. Información tecnológica, 30(6), 111-122. https://dx.doi.org/10.4067/S0718-07642019000600111
Rodó, P. (2019, julio 30). Contraste de Dickey-Fuller. Economipedia.com
Schwarz, G. (1978). Estimating the dimension of a model. The annals of statistics, 461-464. https://www.jstor.org/stable/2958889
Taneja, K., Ahmad, S., Ahmad, K., & Attri, S. (2016). Time series analysis of aerosol optical depth over New Delhi using Box–Jenkins ARIMA modeling approach, Atmospheric Pollution Research, 7(4), 585-596, ISSN 1309-1042, https://doi.org/10.1016/j.apr.2016.02.004.
Vanderschueren, T., Boute, R., Verdonck, T., Baesens, B., & Verbeke, W. (2023). Optimizing the preventive maintenance frequency with causal machine learning, International Journal of Production Economics, 258, ISSN 0925-5273, https://doi.org/10.1016/j.ijpe.2023.108798.
Walls, L., & Bendell, A., (1987). Time series methods in reliability, Reliability Engineering, Volume 18, Issue 4, 1987, Pages 239-265, ISSN 0143-8174, https://doi.org/10.1016/0143-8174(87)90030-8.
Zahedi-Hosseini, F. (2018). Modeling and simulation for the joint maintenance-inventory optimization of production systems. Winter Simulation Conference (WSC). 3264-3274. https://doi:10.1109/wsc.2018.8632283.
Zdenek, V. & Rudolf, H. (2003). Preventive maintenance optimization on the basis of operating data analysis. Annual Reliability and Maintainability Symposium, 400-406. https://doi:10.1109/rams.2003.1182022.
Zhang, Z., Tang, Q., & Chica, M. (2021). Maintenance costs and makespan minimization for assembly permutation flow shop scheduling by considering preventive and corrective maintenance. Journal of Manufacturing Systems, 59, 549 -564. https://doi:10.1016/j.jmsy.2021.03.020