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Improved Fault Detection Model

  • 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 about the improvement in different modules of the fault diagnosis framework. During data acquisition, the data is acquired in cyclic order instead of capturing in one shot. For feature extraction purpose, a new set of 343 features were introduced for detailed analysis. The next step is about to improve the feature selection where we proposed a novel feature selection method based on the graphical analysis. This method is based on how nicely a feature can differentiate between two states based on their feature’s plots for multiple recordings. In the classification phase, each feature votes for a class and the final class is decided based on the majority.

References

  1. 1.
    Verma, N.K., Sevakula, R.K., Dixit, S., Salour, A.: Intelligent condition based monitoring using acoustic signals for air compressors. IEEE Trans. Reliab. 65(1), 291–309 (2016)CrossRefGoogle Scholar
  2. 2.
    Verma, N.K., Sevakula, R.K., Thirukovalluru, R.: Pattern analysis framework with graphical indices for condition-based monitoring. IEEE Trans. Reliab. 66(4), 1085–1100 (2017)CrossRefGoogle Scholar
  3. 3.
    Verma, N.K., Sevakula, R.K., Goel, S.: Study of transforms for their comparison. In: International Conference on Industrial and Information Systems, Gwalior, India, pp. 1–6 (2014)Google Scholar
  4. 4.
    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
  5. 5.
    Sevakula, R.K., Verma, N.K.: Assessing generalization ability of majority vote point classifiers. IEEE Trans. Neural Netw. Learn. Syst. 28(2), 2985–2997 (2017)MathSciNetCrossRefGoogle 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

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