Análisis de fallas usando DFMEA, árbol de fallas y redes bayesianas

Luz Angélica Aguilar Chávez; Manuel Arnoldo Rodríguez Medina; Eduardo Rafael Poblano Ojinaga; Lizette Alvarado Tarango; Jorge Adolfo Pinto Santos

doi:10.61117/ipsumtec.v6i3.204

Authors

Luz Angélica Aguilar Chávez Tecnológico Nacional de México https://orcid.org/0000-0002-7219-6175
Manuel Arnoldo Rodríguez Medina Tecnológico Nacional de México https://orcid.org/0000-0003-1676-0664
Eduardo Rafael Poblano Ojinaga Tecnológico Nacional de México https://orcid.org/0000-0003-3482-7252
Lizette Alvarado Tarango Tecnológico Nacional de México https://orcid.org/0000-0001-7934-8330
Jorge Adolfo Pinto Santos Tecnológico Nacional de México https://orcid.org/0000-0001-9614-2764

DOI:

https://doi.org/10.61117/ipsumtec.v6i3.204

Keywords:

Fault tree, Boolean algebra, DFMEA, Bayesian inference, Bayesian network

Abstract

The automotive industry and its regulations (ISO 9001:2015 and IATF 16949) establish a logical sequence for the quality assurance of its products, which considers the part plan, the design FMEA (DFMEA), the cause and effect matrix, the process flow diagram, the process FMEA (PFMEA) and the control plan. The quality assurance plan is established from the product planning stage, and in the product design stage it is designed according to the design goals and the possible failures that it may have are defined. This document shows the failure analysis of a device using in principle the information obtained from a design FMEA (PFMEA), through the tools of failure tree analysis (FTA) analyzing the relationships between the fundamental elements and their relationships and finally the construction and analysis of a Bayesian Network (BN) where it is allowed to establish and rely on the causal relationships of the variables established for the construction of the model and perform in statistical analysis that allows obtaining information that accounts for the behavior and adaptation that can be made to the model through the use of statistical software such as SamIam which performs conditional probability calculations based on the proposed causal relationships. Through the use and analysis of the above tools, it was possible to find the behavior of the components for a gasoline pump and establish what are the probabilities of failure of each one, as well as its impact on the system and on the variable of primary interest that is the general failure of the pump.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

References

Przytula, K. W., Milford, R. (2006), An Efficient Framework for the Conversion of Fault Trees to Diagnostic Bayesian Network Models, IEEEAC paper No. 1224, Version 1. DOI: https://doi.org/10.1109/AERO.2006.1656103

Langseth, H. (2007). Bayesian networks in reliability. Elsevier. Recuperado de: 10.1016/j.ress.2005.11.037. Lopez, Takeya Bruno (2011) Algebra Booleana, http://www.itnuevolaredo.edu.mx/takeyas DOI: https://doi.org/10.1016/j.ress.2005.11.037

Chengbing He, Runze Wang, Li Ma, Xiaobo Li, Xiaofeng Jiao, Lei Song, (2019), Research on Fault Diagnosis Method Based on FMEA/FTA and Bayesian Network, 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC), IEEE Xplore. Pp.173-177. DOI: https://doi.org/10.1109/SDPC.2019.00039

Rong-xing Duan,Hui-lin Zho, (2012), A New Fault Diagnosis Method Based on Fault Tree and Bayesian Networks, 2012 International Conference on Future Electrical Power and Energy Systems, Elsevier.

Sánchez, F. Alejandra; Tesis Doctoral (2021), Desarrollo de un modelo para determinar la confiabilidad del diagnóstico del vph mediante inferencia bayesiana, División de Estudios de Posgrado e Investigación, Instituto Tecnológico de Ciudad Juárez.

Almond, Rusell, G., Mislevy, Robert J., Steinberg, Linda S., Yan, Duanly, Williamson, David M. (2015), Bayesian Networks in Educational Assessment, Springer. DOI: https://doi.org/10.1007/978-1-4939-2125-6

Bobbioa, A., Portinalea, L., Minichinob, M., Ciancamerla, E., (2001), Improving the analysis of dependable systems by mapping fault trees into Bayesian networks, Reliability Engineering and System Safety 71 (2001) 249–260 DOI: https://doi.org/10.1016/S0951-8320(00)00077-6

Kraisangka, Jidapa and Druzdzel, Marek J. (2018). A Bayesian Network Interpretation of the Cox’s Proportional Hazard Model. This manuscript is made available under the Elsevier user license https://www.elsevier.com/open-access/userlicense/1.0/ DOI: https://doi.org/10.1016/j.ijar.2018.09.007

W. E. Vesely, F. F. Goldberg, N. H. Roberts, D. F. Haasl, (1981). Fault Tree Handbook, U.S. Nuclear Regulatory Commission.

Givehchi, S., Heidari, A. (2018). Bayes Networks and Fault Tree Analysis application in Reliability Estimation (Case Study: Automatic Water Sprinkler System). Environmental Energy and Economic Research. Recuperado de: 10.22097/eeer.2019.160566.1057.

AIAG. Automotive Industry Action Group & VDA. Failure Mode and Effects Analysis FMEA Handbook.

Yang, Guangbin, (2006). LIFE CYCLE RELIABILITY ENGINEERING, JOHN WILEY & SONS, page 212- 229.

Malak Medkour, Azzedine Bouzaouit, Lakhdar Khochmane, Ouafae Bennis (2017). Transformation of Fault Trees into Bayesian Networks Methodology for Fault Diagnosis. Mechanika, Kaunas University of Technology, 23 (6), pp.891-899. ff10.5755/j01.mech.23.6.17281ff. ffhal-02457178f DOI: https://doi.org/10.5755/j01.mech.23.6.17281

Oviedo Bayas, B. W., & Zambrano Vega, C. (2021). Redes bayesianas aplicadas a la predicción de errores en las redes definidas por software. Revista Universidad y Sociedad, 13(2), 419-429.

Pearl, J. (1991). Probabilistic reasoning in intelligent systems: Networks of plausible inference. Artificial Intelligence, 48(1), 117–124. doi:10.1016/0004-3702(91)90084-w DOI: https://doi.org/10.1016/0004-3702(91)90084-W