Development of a methodology for calculating reliability in one of the process areas of the vehicle assembly company called Ciauto Cía. Ltd.
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Published:
Aug 16, 2024
Keywords:
Repairable system, NHPP model, Crow Amsaa, Log-linear, reliability.
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Abstract
The reliability analysis of critical systems in the industrial sector is a very useful tool to improve decision making in the maintenance department. Generally, traditional reliability analysis methods assume restorations of the equipment to its original condition, but in practice this does not happen, since interventions are generally carried out to correct only the failure that occurs at that moment; For this reason, the objective of this research was to develop a methodology to know the current reliability of repairable assets where minimal repairs are carried out, and its prediction for 5 years, with the calculation of the intensity of failures and the average time between failures. The sample was selected from the failure history records from January 2022 to May 2024 of the welding plant of a vehicle assembler, a Jack Knife diagram was made to prioritize the analysis of the systems that cause the most productive stops per repair have generated. A trend test was carried out to determine the bias that the historical data have and thus be able to adjust them to non-homogeneous Poisson stochastic processes, the Crow Amsaa and Log-linear model was used to select the one that best fits the data and is capable of generating forecasts with the lowest possible error. From the study carried out, it was determined that the systems that have caused the most productive stops are the SP-43 and SP-16 welding machines, and the MB-10 JIG. For the SP-43 system, the model that generated the lowest error for a forecast within 5 years was Crow Amsaa with an estimate of 48 failures and one failure every 233 work hours, while for the SP-16 and JIG MB systems -10, the log-linear model presented the best fit, predicting 19 failures, one failure every 987 hours and 22 failures, one every 822 hours of operation respectively.
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Villacrés Parra, S. R., Zavala León, M. A., & Viscaíno Cuzco, M. A. (2024). Development of a methodology for calculating reliability in one of the process areas of the vehicle assembly company called Ciauto Cía. Ltd. Ciencia Digital, 8(3), 137-160. https://doi.org/10.33262/cienciadigital.v8i3.3119
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References
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Wu, J., Dohi, T., & Okamura, H. (2024). A novel lifetime analysis of repairable systems via Daubechies wavelets. Annals of Operations Research, 1–4. https://doi.org/10.1007/s10479-024-06074-6
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Alsultan, F. A., & Sulaiman, M. S. (2024). Bayesian Estimation of Power Law Function in Non-homogeneous Poisson Process Applied in Mosul Gas Power Plant - Iraq. Iraqi Journal of Science, 65(5), 2596–2604. https://doi.org/10.24996/ijs.2024.65.5.20
Bacha, S., & Bellaouar, A. (2023). Assessment of the Effectiveness of Maintenance Actions and the Influence of Covariates on the Reliability of Gas Turbines using the Extended Generalized Proportional Intensity Model. International Journal of Performability Engineering, 19(4), 283–290. https://doi.org/10.23940/ijpe.23.04.p7.283290
Brown, B., Liu, B., McIntyre, S., & Revie, M. (2023). Reliability evaluation of repairable systems considering component heterogeneity using frailty model. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 237(4), 654–670. https://doi.org/10.1177/1748006X221109341
Carlos R Batista-Rodríguez, R. I. U.-S. (2017). Proposal of a bootstrapping methodology to calculate reliability indexes. Ingeniería y Competitividad, 19(2), 102–108.
Chávez-Cadena, M. I., Jiménez-Cargua, J. W., & Cucuri-Pushug, M. I. (2020). Análisis de confiabilidad, mantenibilidad y disponibilidad (CMD) del sistema de reinyección de agua de formación. Revista Arbitrada Interdisciplinaria Koinonía, 5(9), 249. https://doi.org/10.35381/r.k.v5i9.647
Chik, L., Albrecht, D., & Kodikara, J. (2018). Modeling Failures in Water Mains Using the Minimum Monthly Antecedent Precipitation Index. Journal of Water Resources Planning and Management, 144(4), 1–6. https://doi.org/10.1061/(asce)wr.1943-5452.0000926
Cruz, P., Echaveguren, T., & González, P. (2017). Estimación del potencial de rollover de vehículos pesados usando principios de confiabilidad. Revista Ingeniería de Construcción, 32(1), 5–14. https://doi.org/10.4067/s0718-50732017000100001
de Abreu, M. N. G., Esquerre, K. P. S. O. R., Massa, A. R. C. d. G., & Pessoa, R. W. S. (2018). Reliability analysis associated with maintenance of online analyzers. In Computer Aided Chemical Engineering (Vol. 44, Issue 2004). Elsevier Masson SAS. https://doi.org/10.1016/B978-0-444-64241-7.50220-2
Dias, P., Silva, F. J. G., Campilho, R. D. S. G., Ferreira, L. P., & Santos, T. (2019). Analysis and improvement of an assembly line in the automotive industry. Procedia Manufacturing, 38(2019), 1444–1452. https://doi.org/10.1016/j.promfg.2020.01.143
Echeverr, A. (2018). Análisis bibliográfico de la confiabilidad operacional en sistemas técnicos complejos.
Gasca, M. C., Camargo, L. L., & Medina, B. (2017). Sistema para Evaluar la Confiabilidad de Equipos Críticos en el Sector Industrial. Informacion Tecnologica, 28(4), 111–124. https://doi.org/10.4067/S0718-07642017000400014
Hashimoto, T., & Takizawa, S. (2021). Prediction of membrane failure in a water purification plant using nonhomogeneous poisson process models. Membranes, 11(11). https://doi.org/10.3390/membranes11110800
Hou, Y. F., Huang, C. Y., & Fang, C. C. (2022). Using the Methods of Statistical Data Analysis to Improve the Trustworthiness of Software Reliability Modeling. IEEE Access, 10, 25358–25375. https://doi.org/10.1109/ACCESS.2022.3154103
Hu, Z., Mansour, R., Olsson, M., & Du, X. (2021). Second-order reliability methods: a review and comparative study. Structural and Multidisciplinary Optimization, 64(6), 3233–3263. https://doi.org/10.1007/s00158-021-03013-y
Kim, K. S., & Kim, H. C. (2016). The performance analysis of the software reliability NHPP log-linear model depend on viewpoint of the learning effects. Indian Journal of Science and Technology, 9(37). https://doi.org/10.17485/ijst/2016/v9i37/101785
Krivtsov, V. V. (2007). Practical extensions to NHPP application in repairable system reliability analysis. Reliability Engineering and System Safety, 92(5), 560–562. https://doi.org/10.1016/j.ress.2006.05.002
Montalvo, R. B., Villar, L., Armando, L., Concepción, D., Alfonso, A., Ángel, A., Soto, R., & Rodríguez, A. (2022). Modificación de la metodología 6 Sigma para comprobación del rediseño de un filtro rotatorio de un producto biológico. 30, 124–133.
Mun, B. M., Kvam, P. H., & Bae, S. J. (2021). Mixed-Effects Nonhomogeneous Poisson Process Model for Multiple Repairable Systems. IEEE Access, 9, 71900–71908. https://doi.org/10.1109/ACCESS.2021.3077605
Orrantia Daniel, G., Sánchez Leal, J., De la Riva Rodríguez, J., Reyes Martínez, R. M., & Herrera Ríos, E. B. (2022). Metodología para medir la confiabilidad en líneas de ensamble. RIDE Revista Iberoamericana Para La Investigación y El Desarrollo Educativo, 12(24). https://doi.org/10.23913/ride.v12i24.1217
Paez Advincula, R. R. (2022). Importancia de la ingeniería de confiabilidad operacional para el desarrollo empresarial. Industrial Data, 25(1), 137–156. https://doi.org/10.15381/idata.v25i1.21224
Ramírez Montoya, J., Ramos Ramírez, E., & Martínez Salazar, J. L. (2022). Estimación de la función de confiabilidad usando remuestreo Jackknife y transformaciones. Ciencia e Ingeniería Neogranadina, 32(1), 71–82. https://doi.org/10.18359/rcin.5682
Slimacek, V., & Lindqvist, B. H. (2017). Nonhomogeneous Poisson process with nonparametric frailty and covariates. Reliability Engineering and System Safety, 167, 75–83. https://doi.org/10.1016/j.ress.2017.05.026
Soltanali, H., Garmabaki, A. H. S., Thaduri, A., Parida, A., Kumar, U., & Rohani, A. (2019). Sustainable production process: An application of reliability, availability, and maintainability methodologies in automotive manufacturing. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 233(4), 682–697. https://doi.org/10.1177/1748006X18818266
Soltanali, H., Rohani, A., Tabasizadeh, M., Abbaspour-Fard, M. H., & Parida, A. (2020). Operational reliability evaluation-based maintenance planning for automotive production line. Quality Technology & Quantitative Management, 17(2), 186–202. https://doi.org/10.1080/16843703.2019.1567664
Srivastava, N. K., & Mondal, S. (2014). Predictive maintenance using FMECA method and NHPP models. International Journal of Services and Operations Management, 19(3), 319–337. https://doi.org/10.1504/IJSOM.2014.065367
Srivastava, P. W., & Jain, N. (2011). Reliability prediction during development phase of a system. Quality Technology and Quantitative Management, 8(2), 111–124. https://doi.org/10.1080/16843703.2011.11673251
Wu, J., Dohi, T., & Okamura, H. (2024). A novel lifetime analysis of repairable systems via Daubechies wavelets. Annals of Operations Research, 1–4. https://doi.org/10.1007/s10479-024-06074-6
Zuo, K., & Xiao, M. (2022). A repairable multi-state system with a general α-series process and an order-replacement policy. Communications in Statistics - Theory and Methods, 51(20), 7021–7037. https://doi.org/10.1080/03610926.2020.1869991