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Fault Detection and Classification for Induction Motors Using Genetic Programming

  • Yu Zhang
  • Ting HuEmail author
  • Xiaodong Liang
  • Mohammad Zawad Ali
  • Md. Nasmus Sakib Khan Shabbir
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11451)

Abstract

Induction motors are the workhorse in various industry sectors, and their accurate fault detection is essential to ensure reliable operation of critical industrial processes. Since various types of mechanical and electrical faults could occur, induction motor fault diagnosis can be interpreted as a multi-label classification problem. The current and vibration input data collected by monitoring a motor often require signal processing to extract features that can better characterize these waveforms. However, some extracted features may not be relevant to the classification, feature selection is thus necessary. Given such challenges, in recent years, machine learning methods, including decision trees and support vector machines, are increasingly applied to detect and classify induction motor faults. Genetic programming (GP), as a powerful automatic learning algorithm with its abilities of embedded feature selection and multi-label classification, has not been explored to solve this problem. In this paper, we propose a linear GP (LGP) algorithm to search predictive models for motor fault detection and classification. Our method is able to evolve multi-label classifiers with high accuracies using experimentally collected data in the lab by monitoring two induction motors. We also compare the results of the LGP algorithm to other commonly used machine learning algorithms, and are able to show its superior performance on both feature selection and classification.

Keywords

Genetic programming Feature selection Classification Fault detection Induction motor 

Notes

Acknowledgments

This research was supported by Newfoundland and Labrador Research and Development Corporation (RDC) Ignite Grant 5404.1942.101 and the Natural Science and Engineering Research Council (NSERC) of Canada Discovery Grant RGPIN-2016-04699 to TH.

References

  1. 1.
    Wu, S., Chow, T.W.: Induction machine fault detection using som-based RBF neural networks. IEEE Trans. Ind. Electron. 51(1), 183–194 (2004)CrossRefGoogle Scholar
  2. 2.
    Razavi-Far, R., Farajzadeh-Zanjani, M., Saif, M.: An integrated class-imbalanced learning scheme for diagnosing bearing defects in induction motors. IEEE Trans. Ind. Inform. 13(6), 2758–2769 (2017)CrossRefGoogle Scholar
  3. 3.
    Martin-Diaz, I., Morinigo-Sotelo, D., Duque-Perez, O., Romero-Troncoso, R.J.: An experimental comparative evaluation of machine learning techniques for motor fault diagnosis under various operating conditions. IEEE Trans. Ind. Appl. 54(3), 2215–2224 (2018)CrossRefGoogle Scholar
  4. 4.
    Godoy, W.F., da Silva, I.N., Goedtel, A., Palácios, R.H.C., Lopes, T.D.: Application of intelligent tools to detect and classify broken rotor bars in three-phase induction motors fed by an inverter. IET Electr. Power Appl. 10(5), 430–439 (2016)CrossRefGoogle Scholar
  5. 5.
    Poli, R., Langdon, W.B., McPhee, N.F.: A Field Guide to Genetic Programming (2008). http://lulu.com
  6. 6.
    Pappa, G.L., Ochoa, G., Hyde, M.R., Freitas, A.A., Woodward, J., Swan, J.: Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms. Genet. Program. Evol. Mach. 15(1), 3–35 (2014)CrossRefGoogle Scholar
  7. 7.
    Brameier, M., Banzhaf, W.: A comparison of linear genetic programming and neural networks in medical data mining. IEEE Trans. Evol. Comput. 5(1), 17–26 (2001)CrossRefGoogle Scholar
  8. 8.
    Agapitos, A., O’Neill, M., Brabazon, A.: Adaptive distance metrics for nearest neighbour classification based on genetic programming. In: Krawiec, K., Moraglio, A., Hu, T., Etaner-Uyar, A.Ş., Hu, B. (eds.) EuroGP 2013. LNCS, vol. 7831, pp. 1–12. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-37207-0_1CrossRefGoogle Scholar
  9. 9.
    Guven, A.: Linear genetic programming for time-series modelling of daily flow rate. J. Earth Syst. Sci. 118(2), 137–146 (2009)CrossRefGoogle Scholar
  10. 10.
    Nguyen, S., Mei, Y., Zhang, M.: Genetic programming for production scheduling: a survey with a unified framework. Complex Intell. Syst. 3(1), 41–66 (2017)CrossRefGoogle Scholar
  11. 11.
    Parkins, A.D., Nandi, A.K.: Genetic programming techniques for hand written digit recognition. Sig. Process. 84(12), 2345–2365 (2004)CrossRefGoogle Scholar
  12. 12.
    Link, J., et al.: Application of genetic programming to high energy physics event selection. Nucl. Instr. Meth. Phys. Res. Sect. A: Accelerators Spectrometers Detectors Assoc. Equip. 551(2–3), 504–527 (2005)CrossRefGoogle Scholar
  13. 13.
    Chen, S.H., Yeh, C.H.: Evolving traders and the business school with genetic programming: a new architecture of the agent-based artificial stock market. J. Econ. Dyn. Control 25(3–4), 363–393 (2001)CrossRefGoogle Scholar
  14. 14.
    Liu, K.H., Xu, C.G.: A genetic programming-based approach to the classification of multiclass microarray datasets. Bioinformatics 25(3), 331–337 (2009).  https://doi.org/10.1093/bioinformatics/btn644CrossRefGoogle Scholar
  15. 15.
    Hu, T., et al.: An evolutioanry learning and network approach to identifying key metabolites for osteoarthritis. PLoS Comput. Biol. 14(3), e1005986 (2018)CrossRefGoogle Scholar
  16. 16.
    Langdon, W.B., Poli, R.: Foundations of Genetic Programming. Springer, Heidelberg (2013)zbMATHGoogle Scholar
  17. 17.
    Guo, H., Jack, L.B., Nandi, A.K.: Feature generation using genetic programming with application to fault classification. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 35(1), 89–99 (2005)CrossRefGoogle Scholar
  18. 18.
    Witczak, M., Obuchowicz, A., Korbicz, J.: Genetic programming based approaches to identification and fault diagnosis of non-linear dynamic systems. Int. J. Control 75(13), 1012–1031 (2002).  https://doi.org/10.1080/00207170210156224MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Brameier, M.F., Banzhaf, W.: Linear Genetic Programming, 1st edn. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-0-387-31030-5CrossRefzbMATHGoogle Scholar
  20. 20.
    Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA (1992)zbMATHGoogle Scholar
  21. 21.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)zbMATHGoogle Scholar
  22. 22.
    Breiman, L.: Classification and Regression Trees. Routledge, Abingdon (2017)CrossRefGoogle Scholar
  23. 23.
    Liaw, A., Wiener, M., et al.: Classification and regression by randomForest. R News 2(3), 18–22 (2002)Google Scholar
  24. 24.
    Rish, I.: An empirical study of the naive Bayes classifier. In: IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, vol. 3, pp. 41–46. IBM, New York (2001)Google Scholar
  25. 25.
    Ali, M.Z., Shabbir, M.N.S.K., Liang, X., Zhang, Y., Hu, T.: Experimental investigation of machine learning based fault diagnosis for induction motors. In: Proceedings of 2018 IEEE Industry Applications Society (IAS) Annual Meeting, pp. 1–14. IEEE (2018)Google Scholar
  26. 26.
    Ali, M.Z., Shabbir, M.N.S.K., Liang, X., Zhang, Y., Hu, T.: Machine learning based fault diagnosis for single- and multi-faults in induction motors using measured stator currents and vibration signals. IEEE Trans. Ind. Appl. (2019, in press)Google Scholar
  27. 27.
    Li, J., Li, M., Yao, X., Wang, H.: An adaptive randomized orthogonal matching pursuit algorithm with sliding window for rolling bearing fault diagnosis. IEEE Access 6, 41107–41117 (2018)CrossRefGoogle Scholar
  28. 28.
    Mallat, S., Zhang, Z.: Matching pursuit with time-frequency dictionaries. Technical report, Courant Institute of Mathematical Sciences, New York, United States (1993)Google Scholar
  29. 29.
    Buckland, M., Gey, F.: The relationship between recall and precision. J. Am. Soc. Inf. Sci. 45(1), 12–19 (1994)CrossRefGoogle Scholar
  30. 30.
    Powers, D.M.: Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. J. Mach. Learn. Technol. 2, 37–63 (2011)Google Scholar
  31. 31.
    Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetzbMATHGoogle Scholar
  32. 32.
    Hu, T., Oksanen, K., Zhang, W., Randell, E., Furey, A., Zhai, G.: Analyzing feature importance for metabolomics using genetic programming. In: Castelli, M., Sekanina, L., Zhang, M., Cagnoni, S., García-Sánchez, P. (eds.) EuroGP 2018. LNCS, vol. 10781, pp. 68–83. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-77553-1_5CrossRefGoogle Scholar
  33. 33.
    Wilcoxon, F.: Individual comparisons by ranking methods. Biometr. Bull. 1(6), 80–83 (1945)CrossRefGoogle Scholar
  34. 34.
    Wilcoxon, F., Katti, S., Wilcox, R.A.: Critical values and probability levels for the Wilcoxon rank sum test and the Wilcoxon signed rank test. Sel. Tables Math. Stat. 1, 171–259 (1970)zbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yu Zhang
    • 1
  • Ting Hu
    • 1
    Email author
  • Xiaodong Liang
    • 2
  • Mohammad Zawad Ali
    • 2
  • Md. Nasmus Sakib Khan Shabbir
    • 2
  1. 1.Department of Computer ScienceMemorial UniversitySt. John’sCanada
  2. 2.Department of Electrical and Computer EngineeringMemorial UniversitySt. John’sCanada

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