An Expert System Based on Parametric Net to Support Motor Pump Multi-Failure Diagnostic

  • Flavia Cristina Bernardini
  • Ana Cristina Bicharra Garcia
  • Inhaúma Neves Ferraz
Part of the IFIP International Federation for Information Processing book series (IFIPAICT, volume 296)


Early failure detection in motor pumps is an important issue in prediction maintenance. An efficient condition-monitoring scheme is capable of providing warning and predicting the faults at early stages. Usually, this task is executed by humans. The logical progression of the condition-monitoring technologies is the automation of the diagnostic process. To automate the diagnostic process, intelligent diagnostic systems are used. Many researchers have explored artificial intelligence techniques to diagnose failures in general. However, all papers found in literature are related to a specific problem that can appear in many different machines. In real applications, when the expert analyzes a machine, not only one problem appears, but more than one problem may appear together. So, it is necessary to propose new methods to assist diagnosis looking for a set of occurring fails. For some failures, there are not sufficient instances that can ensure good classifiers induced by available machine learning algorithms. In this work, we propose a method to assist fault diagnoses in motor pumps, based on vibration signal analysis, using expert systems. To attend the problems related to motor pump analyses, we propose a parametric net model for multi-label problems. We also show a case study in this work, showing the applicability of our proposed method.


Expert System Machine Learning Algorithm Abstract Feature Machine Fault Motor Pump 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Batista, G.; Prati, R. C.; Monard, M. C. A study of the behavior of several methods for balancing machine learning training data. SIGKDD Explorations, 6(1):20–29, 2004.CrossRefGoogle Scholar
  2. 2.
    Brinkler, K; Hullermeier, E. “Case-Based Multilabel Ranking”. In: Proceedings 20th International Conference on Artificial Intelligence (IJCAI ′07), pp. 702–707 (2007).Google Scholar
  3. 3.
    Garcia, A. C. B. “Active Design Documents: A New Approach for Supporting Documentation in Preliminary Routine Design”, PhD thesis, Stanford University, (1992).Google Scholar
  4. 4.
    Kowalski, C. T.; Orlowska-Kowalska, T. “Neural networks application for induction motor faults diagnosis”. Mathematics and Computers in Simulation, 63:435–448, 2003.MathSciNetCrossRefGoogle Scholar
  5. 5.
    Li, B.; Chow, M.; Tipsuwan, Y; Hung, J.C. “Neural-Network-Based Motor Rolling Bearing Fault Diagnosis”. IEEE Transactions on Industrial Electronics, 47(5), 2000.Google Scholar
  6. 6.
    Mendel, E.; Mariano, L. Z.; Drago, I.; Loureiro, S.; Rauber, T. W.; Varejão, F. M.; Batista, R.J. “Automatic Bearing Fault Pattern Recognition Using Vibration Signal Analysis”. In: Proceedings IEEE International Symposium on Industrial Electronics (ISIE′08), Cambridge, pp. 955–960 (2008).Google Scholar
  7. 7.
    Mitchell, J. S. Introduction to Machinery Analysis and Monitoring, PenWel Books, Tulsa (1993).Google Scholar
  8. 8.
    Mitchell, T. Machine Learning. McGraw Hill (1997).Google Scholar
  9. 9.
    Sebastiani, F. “Machine learning in automated text categorization”. ACM Computing Surveys. 34(1):1–47, 2002.CrossRefGoogle Scholar
  10. 10.
    Singh, G.K.; Kazzaz, S. A. S. A. “Induction machine drive condition monitoring and diagnostic research — a survey”. Electric Power Systems Research, 64:145–158, 2003.CrossRefGoogle Scholar
  11. 11.
    Schapire, R. E.; Singer, Y.. “BoosTexter: A boosting-based system for text categorization”. Machine Learning, 39(2/3):135–168, 2000.CrossRefMATHGoogle Scholar
  12. 12.
    X. Shen, M. Boutell, J. Luo, and C. Brown “Multi label Machine learning and its application to semantic scene classification”. In: Proceedings of the 2004 International Symposium on Electronic Imaging (EI 2004), pp. 18–22 (2004).Google Scholar
  13. 13.
    Zhang, S.; Ganesan, R.; Xistris, G. D. “Self-Organizing Neural Networks for Automated Machinery Monitoring Systems”. Mechanical Systems and Signal Processing, 10(5):517– 532, 1996.CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2009

Authors and Affiliations

  • Flavia Cristina Bernardini
    • 1
  • Ana Cristina Bicharra Garcia
    • 1
  • Inhaúma Neves Ferraz
    • 1
  1. 1.ADDLabs — Active Documentation Design LaboratoryUFF — Universidade Federal FluminenseBoa Viagem, NiteróiBrazil

Personalised recommendations