Abstract

This paper presents a DC motor fault diagnosis system based on Bayesian networks. This was done by the design of a new electromechanical test bed allowing the collection of functioning data from a real world industrial Direct current (DC) Motor. The data collection will help in the construction of Bayesian networks models. These data are collected from sensors measuring different types of variables that are directly related to the industrial system. Without doing any mathematical modeling that describes the physical properties of the studied DC motor, the proposed tool provides with the help of Bayesian networks parameters and structure learning algorithms, the base to construct a fault diagnosis tool that can be extended to a fault prognosis tool.

References

• E Afjei, A Karami, et al. Sensorless speed/position control of brushed dc motor. In Electrical Machines and Power Electronics, 2007. ACEMP’07. International Aegean Conference on, pages 730–732. IEEE, 2007.
• Abdelkabir BACHA, A.Haroun SABRY, and Jamal BENHRA. Contribution l’aide la d´ecision dans le domaine industriel en utilisant les r´eseaux bay´esiens. In CIMSI 14 Conf´erence Internationnale sur le Monitoring des Systmes Industriels.
• Gregory F Cooper. The computational complexity of probabilistic inference using bayesian belief networks. Artificial intelligence, 42(2):393–405, 1990.
• Gregory F Cooper and Edward Herskovits. A bayesian method for the induction of probabilistic networks from data. Machine learning, 9(4):309–347, 1992.
• Marcos FSV DAngelo, Reinaldo M Palhares, Luciana B Cosme, Lucas A Aguiar, Felipe S Fonseca, and Walmir M Caminhas. Fault detection in dynamic systems by a fuzzy/bayesian network formulation. Applied Soft Computing, 21:647–653, 2014.
• M Julia Flores, Jos´e A G´amez, Ana M Mart´inez, and Jos´e M Puerta. Handling numeric attributes when comparing bayesian network classifiers: does the discretization method matter? Applied Intelligence, 34(3):372–385, 2011.
• Nir Friedman, Moises Goldszmidt, et al. Discretizing continuous attributes while learning bayesian networks. In ICML, pages 157–165, 1996.
• Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, and Ian H Witten. The weka data mining software: an update. ACM SIGKDD explorations newsletter, 11(1):10–18, 2009.
• David Heckerman, Dan Geiger, and David M Chickering. Learning bayesian networks: The combination of knowledge and statistical data. Machine learning, 20(3):197–243, 1995.
• Geoffrey Holmes, Andrew Donkin, and Ian H Witten. Weka: A machine learning workbench. In Intelligent Information Systems, 1994. Proceedings of the 1994 Second Australian and New Zealand Conference on, pages 357–361. IEEE, 1994.
• Khalid Iqbal, Xu-Cheng Yin, Hong-Wei Hao, Qazi Mudassar Ilyas, and Hazrat Ali. An overview of bayesian network applications in uncertain domains. International Journal of Computer Theory and Engineering, 7(6):416, 2015.
• Patrick Jahnke. Machine Learning Approaches for Failure Type Detection and Predictive Maintenance. PhD thesis, tudarmstadt, 2015.
• Andrew KS Jardine, Daming Lin, and Dragan Banjevic. A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical systems and signal processing, 20(7):1483–1510, 2006.
• Kiran R Karkera. Building Probabilistic Graphical Models with Python. Packt Publishing Ltd, 2014.
• Daphne Koller and Nir Friedman. Probabilistic graphical models: principles and techniques. MIT press, 2009.
• Philippe Leray. R´eseaux bay´esiens: apprentissage et mod´elisation de syst`emes complexes. PhD thesis, Universit´e de Rouen, 2006.
• Stefano Monti and Gregory F Cooper. A multivariate discretization method for learning bayesian networks from mixed data. In Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence, pages 404–413. Morgan Kaufmann Publishers Inc., 1998.
• Kevin P Murphy. Machine learning: a probabilistic perspective. MIT press, 2012.
• Patrick Na¨im, Pierre-Henri Wuillemin, Philippe Leray, Olivier Pourret, and Anna Becker. R´eseaux bay´esiens. Paris: Eyrolles, 1999.
• Xiao-Sheng Si, Wenbin Wang, Chang-Hua Hu, and Dong- Hua Zhou. Remaining useful life estimation–a review on the statistical data driven approaches. European Journal of Operational Research, 213(1):1–14, 2011.