ANALYSIS OF HISTORICAL FAILURE DATA AS A TOOL FOR ESTABLISHING PREVENTIVE MAINTENANCE SCHEDULES
DOI:
https://doi.org/10.61799/2216-0388.1997Keywords:
Data Science, Maintenance Strategy, Probability Model.Abstract
Historical failure data of industrial equipment constitutes a fundamental input for statistical analysis aimed at optimizing preventive maintenance strategies. This study follows a quantitative research approach with a correlational scope and is based on the analysis of historical failure records of electric motors with similar operational characteristics, obtained from maintenance databases of an industrial production plant. The data collection technique corresponds to documentary reviews of historical maintenance records. The failure data were fitted to a continuous probability model, specifically the Weibull distribution, to estimate failure probabilities and reliability levels as a function of time. The results show an adequate fitness of the data to the Weibull distribution, with estimated parameters β = 2.058 and α = 707.0037, which allowed the calculation of increasing failure probabilities over time, reaching values above 90% near the expected useful life of the equipment. These results provide quantitative support for evaluating current preventive maintenance frequencies. It is concluded that statistical analysis of historical failure data enables the objective establishment and adjustment of preventive maintenance schedules, contributing to cost reduction and improvement of equipment reliability in industrial environments.
Downloads
References
[1] R. B. Abernethy, The New Weibull Handbook, 5th ed. Tucson, AZ: ReliaSoft Publishing, 2006.
[2] R. K. Mobley, An Introduction to Predictive Maintenance, 2nd ed. Boston, MA: Butterworth-Heinemann, 2002.
[3] L. Silva and R. Pinheiro, “Reliability applied to the Preventive Maintenance on Wind Farms using Weibull distribution with a Financial Approach,” Renewable Energy and Power Quality Journal, vol. 1, pp. 340–343, Apr. 2017, doi: 10.24084/repqj15.314.
[4] A. K. S. Jardine, D. Lin, and D. Banjevic, “A review on machinery diagnostics and prognostics implementing condition-based maintenance,” Mech Syst Signal Process, vol. 20, no. 7, pp. 1483–1510, 2006, doi: https://doi.org/10.1016/j.ymssp.2005.09.012.
[5] H. Rinne, The Weibull Distribution: A Handbook. Boca Raton, FL: CRC Press, 2009.
[6] L. C. Blanco, Probabilidad. Bogotá, Colombia: Universidad Nacional de Colombia, 2004.
[7] J. Serrano, “Comparación de métodos para determinar los parámetros de Weibull para la generación de energía eólica,” Scientia et Technica, vol. 18, no. 2, pp. 384–389, 2013.
[8] D. A. Lind, W. G. Marchal, and S. A. Wathen, Estadística aplicada a los negocios y la economía. México: McGraw-Hill, 2012.
[9] P. D. T. O’Connor and A. V Kleyner, Practical Reliability Engineering. Wiley, 2025. [Online]. Available: https://books.google.com.co/books?id=-NpZEQAAQBAJ
[10] T. P. Carvalho, F. A. A. M. N. Soares, R. Vita, R. da P. Francisco, J. P. Basto, and S. G. S. Alcalá, “A systematic literature review of machine learning methods applied to predictive maintenance,” Comput Ind Eng, vol. 137, p. 106024, 2019, doi: https://doi.org/10.1016/j.cie.2019.106024.
[11] D. C. Montgomery and G. C. Runger, Applied Statistics and Probability for Engineers. Hoboken, NJ: John Wiley & Sons, Inc., 2018.
[12] A. M. Law, Simulation Modeling and Analysis. in McGraw-Hill series in industrial engineering and management science. McGraw-Hill Education, 2015. [Online]. Available: https://books.google.com.co/books?id=0qdNMAEACAAJ
[13] W. Q. Meeker, L. A. Escobar, and F. G. Pascual, Statistical Methods for Reliability Data. in Wiley Series in Probability and Statistics. Wiley, 2021. [Online]. Available: https://books.google.com.co/books?id=39ZKEAAAQBAJ
[14] N. Wayne, Applied Life Data Analysis. in Wiley Series in Probability and Statistics. Wiley, 2003. [Online]. Available: https://books.google.com.co/books?id=qyyjJIL40S0C
[15] J. D. Gibbons and S. Chakraborti, “Nonparametric Statistical Inference,” in International Encyclopedia of Statistical Science, M. Lovric, Ed., Berlin, Heidelberg: Springer Berlin Heidelberg, 2011, pp. 977–979. doi: 10.1007/978-3-642-04898-2_420.
[16] Y. Dodge, The Concise Encyclopedia of Statistics. in The Concise Encyclopedia of Statistics. Springer New York, 2008. [Online]. Available: https://books.google.com.co/books?id=k2zklGOBRDwC
[17] E. Castaño, L. Palmera, T. Velásquez, y D. Ríos, “Modelo de gestión de un cuadro de mando integral de servicios de TI para empresas del sector transporte”, Revista Temario Científico, 5(2), 2025. e25522. DOI: https://doi.org/10.47212/rtcAlinin.3.225.15
[18] U. Escalante-Manosalva, H. F. Noriega-Quintana, J. Flórez-Vergara and O. A. Numa “Estudio socioeconómico para la viabilidad de una plaza de mercado satélite en Aguachica, Cesar”, Mundo Fesc, vol. 14, no. 30, 2024, pp. 8-29 doi: 10.61799/2216-0388.1720.
[19] C. W. Zhang, “Weibull parameter estimation and reliability analysis with zero-failure data from high-quality products,” Reliab Eng Syst Saf, vol. 207, p. 107321, 2021, doi: https://doi.org/10.1016/j.ress.2020.107321.
[20] P. H. Tsarouhas, “Reliability, availability and maintainability analysis in food production lines: A review,” Int J Food Sci Technol, vol. 47, no. 11, pp. 2243–2251, 2012, doi: 10.1111/j.1365-2621.2012.03073.x.
[21] R. K. Mobley, An Introduction to Predictive Maintenance. in Plant Engineering. Butterworth-Heinemann, 2002. [Online]. Available: https://books.google.com.co/books?id=SjqXzxpAzSQC
[22] Y. M. Gómez, D. I. Gallardo, C. Marchant, L. Sánchez, and M. Bourguignon, “An in-depth review of the Weibull model with a focus on various parameterizations,” Mathematics, vol. 12, no. 1, p. 56, 2024, doi: 10.3390/math12010056.
[23] M. G. M. Khan and M. R. Ahmed, “Bayesian method for estimating Weibull parameters for wind resource assessment in a tropical region: A comparison between two-parameter and three-parameter Weibull distributions,” Wind Energy Science, vol. 8, pp. 1277–1298, 2023, doi: 10.5194/wes-8-1277-20
Published
Issue
Section
License
Copyright (c) 2026 Mundo FESC Journal
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
