Advertisement

Fault Recognition

  • Nishchal K. VermaEmail author
  • Al Salour
Chapter
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 256)

Abstract

This chapter details the last module of the fault diagnosis framework, i.e., fault recognition. After the collection of relevant subsets of the features, as detailed in the Chap.  5, classification is performed to decide the machine state. Chapter  2 provides details about different kinds of machine faults. Classification is the task to categorize the given object by learning the relationship between the selected set of features and their class label. Since fault recognition is also treated as a classification process; here, the description of different classification techniques, such as k-means clustering, k-nearest neighbour (k-NN), Naive Bayes classifier, Support Vector Machine (SVM), Multiclass classification algorithms, etc., is provided in this chapter.

References

  1. 1.
    Verma, N.K., Roy, A., Salour, A.: An optimized fault diagnosis method for reciprocating air compressors based on SVM. In: Proceedings of the IEEE Control and System Graduate Research Colloquium Incorporating 2011 IEEE International Conference on System Engineering and Technology, Selangor, Malaysia, pp. 65–69 (2011)Google Scholar
  2. 2.
    Sevakula, R.K., Verma, N.K.: Wavelet transforms for fault detection using SVM in power systems. In: IEEE International Conference on Power Electronics, Drives and Energy Systems, Bengaluru, India, pp. 1–6 (2012)Google Scholar
  3. 3.
    Verma, N.K., Gupta, V.K., Sharma, M., Sevakula, R.K.: Intelligent condition based monitoring of rotating machines using sparse auto-encoders. In: IEEE Conference on Prognostics and Health Management, Maryland, USA, pp. 1–7 (2013)Google Scholar
  4. 4.
    Jamal, A., Verma, N.K.: Automatic fault diagnosis system using acoustic data. In: IEEE 8th International Conference on Industrial and Information Systems, Kandy, Sri Lanka, pp. 421–426 (2013)Google Scholar
  5. 5.
    Verma, N.K., Agrawal, A.K., Sevakula, R.K., Prakash, D., Salour, A.: Improved signal preprocessing techniques for machine fault diagnosis. In: 2013 IEEE 8th International Conference on Industrial and Information Systems, pp. 403–408 (2013)Google Scholar
  6. 6.
    Ramkumar, A.J., Verma, N.K., Dixit, S.: Detection and classification for faults in drilling process using vibration analysis. In: IEEE Conference on Prognostics and Health Management, Washington, USA, pp. 1–6 (2014)Google Scholar
  7. 7.
    Thirukovalluru, R., Sevakula, R.K., Dixit, S., Verma, N.K.: Generating optimum feature sets for fault diagnosis using denoising stacked auto-encoder. In: IEEE International Conference on Prognostics and Health Management, Canada, USA, pp. 1–7 (2016)Google Scholar
  8. 8.
    Agarwal, A., Verma, N.K.: Generalization ability of majority vote point classifiers for motor fault diagnosis. In: IEEE International Conference on Industrial and Information Systems (ICIIS), IIT Roorkee, India, pp. 844–849 (2016)Google Scholar
  9. 9.
    Saraswat, G., Maurya, S., Verma, N.K.: Health monitoring of main battle tank engine using Mamdani type fuzzy model. In 2017 International Conference on Computational Intelligence: Theories, Applications and Future Directions, IIT Kanpur, India, pp. 403–414 (2017)Google Scholar
  10. 10.
    Maurya, S., Singh, V., Dixit, S., Verma, N.K., Salour, A., Liu, J.: Fusion of low-level features with stacked autoencoder for condition based monitoring of machines. In: IEEE International Conference on Prognostics and Health Management, Washington, USA, pp. 1–8 (2018)Google Scholar
  11. 11.
    Sharma, A.K., Singh, V., Verma, NK., Liu, J.: Condition based monitoring of machine using Mamdani fuzzy network. In: IEEE Prognostics and System Health Management Conference, Chongqing, China, pp. 1159–1163 (2018)Google Scholar
  12. 12.
    Verma, N.K., Dixit, S., Sevakula, R.K., Salour, A.: Computational framework for machine fault diagnosis with autoencoder variants. In: 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control, Xi’an, China, pp. 353–358 (2018)Google Scholar
  13. 13.
    Verma, N.K., Sevakula, R.K., Dixit, S., Salour, A.: Intelligent condition based monitoring using acoustic signals for air compressors. IEEE Trans. Rel. 65(1), 291–309 (2016)CrossRefGoogle Scholar
  14. 14.
    Verma, N.K., Sevakula, R.K., Thirukovalluru, R.: Pattern analysis framework with graphical indices for condition based monitoring. IEEE Trans. Rel. 66(4), 1085–1100 (2017)CrossRefGoogle Scholar
  15. 15.
    Sevakula, R.K., Verma, N.K.: Assessing generalization ability of majority vote point classifiers. IEEE Trans. Neural Netw. Learn. Syst. 28(12), 2985–2997 (2017)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Verma, N.K., Roy, A.: Self-optimal clustering technique using optimized threshold function. IEEE Syst. J. 99, 1–14 (2013)Google Scholar
  17. 17.
    Sevakula, R.K., Verma, N.K.: Support vector machine for large databases as classifier. In: International Conference on Swarm, Evolutionary, and Memetic Computing, Bhubaneswar, India, pp. 303–313 (2012)Google Scholar
  18. 18.
    Verma, N.K., Singh, S., Gupta, J.K., Sevakula, R.K., Dixit, S., Salour, A.: Smartphone application for fault recognition. In: 6th International Conference on Sensing Technology, Kolkata, India, pp. 1–6 (2012)Google Scholar
  19. 19.
    Verma, N.K., Singh, J.V., Gupta, M., Sevakula, R.K., Dixit, S.: Windows mobile and tablet app for acoustic signature machine health monitoring. In: International Conference on Industrial and Information Systems, Gwalior, India, pp. 1–6 (2014)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Electrical Engineering and Inter-disciplinary Program in Cognitive ScienceIndian Institute of Technology KanpurKanpurIndia
  2. 2.Boeing Research and TechnologySaint LouisUSA

Personalised recommendations