Derivation of Fuzzy Diagnosis Rules for Multifunctional Fuel Cell Systems

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Published Jul 8, 2014
Christian Modest Frank Thielecke

Abstract

This paper presents a model-based approach for the derivation of fuzzy diagnosis rules. These are used to classify data of faulty system behavior in order to identify root causes. The data is gained from an extended simulation model of a multifunctional fuel cell system for aircraft use. Faulty behavior is implemented into each component and a bottom up simulation is carried out. The data gained is classified according to root causes. This means that each data vector is assigned to a class representing one type of simulated fault. The classified data is then fed into an evolutionary optimization procedure. There it is weighted and separated into training and validation data. Inside the optimization procedure, the structure of the fuzzy diagnosis rule is represented by a chromosome that has a discrete and a real valued part. The discrete part describes the selection of a signal and the real valued part states parameters of the membership function for each signal. Based on training data, a genetic algorithm optimizes both parts and a set of optimal binary and real valued parameters is gained. By that, one fuzzy diagnosis rule at a time is identified that best
fits a set of fitness functions. On basis of this rule, weights of the training data are updated afterwards. This is done in order to guide the genetic algorithm in the next run to data vectors that are not covered effectively yet. Each run of the algorithm gives a new fuzzy diagnosis rule. The performance of the set of all rules that are gained so far is evaluated by use of validation data. Subsequently, a new run is started. This process continues until a stop criterion is reached. A set of optimal fuzzy diagnosis rules is gained in the end.

How to Cite

Modest, C., & Thielecke, F. (2014). Derivation of Fuzzy Diagnosis Rules for Multifunctional Fuel Cell Systems. PHM Society European Conference, 2(1). https://doi.org/10.36001/phme.2014.v2i1.1515
Abstract 457 | PDF Downloads 123

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Keywords

Automatic diagnostics, Intelligent Health Monitoring, Behavior modeling, Fuzzy Inference, Fuel Cell System

References
Andres Pena-Reyes, C., & Sipper, M. (1999). A Fuzzy-Genetic Approach to Breast Cancer Diagnosis. Artificial Intelligence in Medicine, 17(2), 131–155.
Coello, C. A. C., Lamont, G. B., & van Veldhuizen, D. A. (2007). Evolutionary Algorithms for Solving Multi-Objective Problems (2nd ed.). Boston and MA: Springer Science+Business Media, LLC.
Cox, E. (1994). The Fuzzy Systems Handbook: A Practitioner’s Guide to Building, Using, and Maintaining Fuzzy Systems. San Diego, CA, USA: Academic Press Professional, Inc.
Enzinger, M. (2010). Technology Programmes-Multifunctional Fuel Cell Application. Hamburg. European Organisation for the Safety of Air Navigation. (2013a, March). Coda Digest - Delays to Air Transport in Europe - Annual 2012 (Tech. Rep.). Brussels, Belgium: Eurocontrol-Central Office for Delay Analysis.
European Organisation for the Safety of Air Navigation. (2013b, May). Performance Review Report - An Assessment of Air Traffic Management in Europe during the Calender Year 2012 (Tech. Rep.). Brussels, Belgium: Eurocontrol-Performance Review Commission.
European Commission. (2001, January). European Aeronautics: A Vision for 2020. Report of the group of personalities.
European Commission. (2011). Flightpath 2050 - Europe’s Vision for Aviation. Report of the high level group on aviation research.
Gonzles, A., & Francisco, H. (1997). Multi-Stage Genetic Fuzzy Systems based on the Iterative Rule Learning Approach. Mathware and Soft Computing, 4(3), 233–249.
Grymlas, J., & Thielecke, F. (2013). Virtual Integration and Testing of Multifunctional Fuel Cell Systems in Commercial Aircraft. SAE International Journal of Aerospace, 6(2), 746–760.
Hastie, T., Tibshirani, R., & Friedman, J. H. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed.). New York: Springer.
Herrera, F., Lozano, M., & Verdegay, J. L. (1995). Generating Fuzzy Rules from Examples using Genetic Algorithms. Fuzzy Logic and Soft Computing, 4, 11–20.
Herwig, H. (2006). Str¨omungsmechanik: Eine Einf¨uhrung in die Physik und die mathematische Modellierung von Str¨omungen (2nd ed.). Berlin: Springer.
Michalewicz, Z. (1996). Genetic Algorithms + Data Structures = Evolution Programs (3rd ed.). Berlin: Springer.
Modest, C., & Thielecke, F. (2012). A Design Methodology of Optimized Diagnosis Functions for High Lift Actuation Systems. In I. Roychoudhury, J. R. Celaya, & A. Saxena (Eds.), Proceedings of the Annual Conference of the Prognostics and Health Management Society 2012 (pp. 233–248). PHM Society.
Stavrakoudis, D., Theocharis, J., & Zalidis, G. (2009). Genetic Fuzzy Rule-based Classifiers for Land Cover Classification from Multispectral Images. In S. G. Tzafestas & K. P. Valavanis (Eds.), Applications of Intelligent Control to Engineering Systems (Vol. 39, p. 195-221). Dordrecht: Springer Netherlands.
Section
Technical Papers

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