Weibull Distribution: Reliability Centered Maintenance and the Use of Bayesian networks



Reliability, Bayesian Networks, Maintenance, Weibull distribution


This article, through the literature review, we discuss Bayesian networks and Weibull distribution to describe the processes of Reliability Centered Maintenance as well as the tools of information technology necessary for their support. To maximize asset reliability cost-effectively, maintenance should be scheduled based on the likely deterioration of an asset. Maintenance management reliability centered aims to increase the availability of the physical item in which it is applied. Since the reliability function of the quality of the program or maintenance plan is a systematic methodology that makes use of IT tools to optimize strategic actions to minimize maintenance costs. Concerning the application of reliability-centered maintenance, this paper presents considerations that allow drawing requires the use of various mathematical tools.


Duarte, A.M.P. and Strong, M. Z. (2009). Implementation methodology of reliability-centered maintenance is integrated total productive maintenance: A case study. XXIV Brazilian Congress of Maintenance. Annals. Recife, Pernambuco.

Flaih, A., Elsalloukh, H., Mendi, E. and Milanova, M. (2012). The Expenetated Inverted Weibull Distribution, Aplied Mathematics & Information Sciences, 6 (2012):167-171.

Pintelon, L; Gelders, L. (1992). Maintenance management decision making. European Journal of Operational Research, v. 58, p.301-317.

Russell, S. and Norvig, P. (2004). Artificial Intelligence. Translation PubliCare Consulting. Rio de Janeiro. Elsevier.

Pear, J.P. (1988). Behavioral stereotypy and the generalized matching equation. Journal of the Experimental Analyses of Behavior.v.50,(1):87-95.

Siqueira, I.P. (2005) Reliability Centered Maintenance: Implementation Manual. Rio de Janeiro: Qualitymark.

Lazakis, I., (2011). Establishing an innovative and integrated reliability and criticality based maintenance strategy for the maritime industry: A PhD thesis. Glasgow, UK: University of Strathclyde, Department of Naval Architecture and Marine Engineering.

Almeida, A. T.; Souza, F.C.M. (2001). Maintenance management towards competitiveness. Recife, University Press.

Aboura, K., and Agbinya, J. I. (2013). Adaptive maintenance optimization using initial reliability estimates. Journal of Green Engineering. 3, 325-345.

Weibull, W. A (1951). Statistical Distribution Function of Wide Applicability. Journal of Applied Mechanics, 18(1):293-297.

Bleakie, A. and Djudjanovic, D., 2013. Feature extraction, condition monitoring, and fault modeling in semiconductor manufacturing systems. Computers in Industry, 64(3), pp. 203-213.

Firmino, P. R. A. (2004). Thesis UFPE. Bayesian Networks for the parameterization of reliability in complex systems.

Poropudas, J. and Virtanen, K., 2011. Simulation metamodeling with dynamic Bayesian networks. European Journal of Operational Research, 214(3), pp. 644-655.

Weber, P.; Medina-Oliva, G., Simon, C.; Iung, B. (2012); “Overview on Bayesian networks appltions for dependability, risk analysis and maintenance areasâ€, Engineering Applications of Artificial Intelligence, 25(1):671–682.

Schreiber, J. N. C.; Wazlawick, R. and Borges, P. S. S. (2002). A proposed adaptive web navigation using Bayesian networks. In: Iberoamerican Congress of educational computing.

Rocha,C.A. (2004). Data Mining. Pará. Available at:

<http://www.laps.ufpa.br/aldebaro/classes/mineracao2sem04/Alex-Bayes. ppt>. Accessed on: 17.06.2016.

Kelly, D.L. and Smith, C.L. (2009). Bayesian inference in probabilistic risk assessment: The current state of the art, Reliability Engineering and System Safety 94(2): 628–643.

Marques, R. L.; Dutra, I. (2000). Redes Bayesianas: o que são, para que servem, algoritmos e exemplos de aplicações. UFRJ.

Wang W & Zhang W. (2008) An asset residual life prediction model based on expert judgments. European Journal of Operational Research, 188(2), 496-505.

Kulkarni S.S. and Achenbach JD. (2008) Structural health monitoring and damage prognosis in fatigue. Structural Health Monitoring, 7(1), 37-49.

Hugin Expert. (2011). Denmark. Available at: <http://www.hugin.com>. Acessed 17.6.2016.

Luna, J. E. O. (2004). Algorithms for learning Bayesian networks from incomplete data. Campo Grande. Master Thesis in Computer Science from the Federal University of Mato Grosso do Sul.




How to Cite

Pozo, H., & Silva, O. R. da. (2017). Weibull Distribution: Reliability Centered Maintenance and the Use of Bayesian networks. Asian Journal of Applied Sciences, 5(5). Retrieved from https://ajouronline.com/index.php/AJAS/article/view/5004