Skip to main content

Weighted ANN Input Layer for Adaptive Features Selection for Robust Fault Classification

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 9490)

Abstract

Model based feature selection for identification of diverse faults in rotary machines can significantly cost time and money and it is nearly impossible to model all faults under different operating environments. In this paper, feedforward ANN input-layer-weights have been used for the adaptive selection of the least number of features, without fault model information, reducing the computations significantly but assuring the required accuracy by mitigating the noise. In the proposed approach, under the assumption that presented features should be translation invariant, ANN uses entire set of spectral features from raw input vibration signal for training. Dominant features are then selected using input-layer-weights relative to a threshold value vector. Different instances of ANN are then trained and tested to calculate F1_score with the reduced dominant features at different SNRs for each threshold value. Trained ANN with best average classification accuracy among all ANN instances gives us required number of dominant features.

Keywords

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Li, B., Chow, M.Y., Tipsuwan, Y., Hung, J.C.: Neural-network-based motor rolling bearing fault diagnosis. IEEE Trans. Ind. Electron. 47(5), 1060–1069 (2000)

    Article  Google Scholar 

  2. Bellini, A., Immovilli, F., Rubini, R., Tassoni, C.: Diagnosis of bearing faults of induction machines by vibration or current signals: a critical comparison. In: Industry Applications Society Annual Meeting, pp. 1–8 (2008)

    Google Scholar 

  3. Yiakopoulos, C.T., Gryllias, K.C., Antoniadis, I.A.: Rolling element bearing fault detection in industrial environments based on a K-means clustering approach. Expert Syst. Appl. 38(3), 2888–2911 (2011)

    Article  Google Scholar 

  4. Bin, Z., Sconyers, C., Byington, C., Patrick, R., Orchard, M., Vachtsevanos, G.: A probabilistic fault detection approach: application to bearing fault detection. IEEE Trans. Ind. Electron. 58(5), 2011–2018 (2011)

    Article  Google Scholar 

  5. Amar, M., Gondal, I., Willson, C.: Vibration spectrum imaging: a bearing fault classification approach. IEEE Trans. Ind. Electron. 62(1), 494–502 (2015)

    Article  Google Scholar 

  6. Amar, M., Gondal, I., Willson, C.: Multi-size-window spectral augmentation: neural network bearing fault classifier. In: The 8th IEEE Conference on Industrial Electronics and Applications, pp. 261–266 (2013)

    Google Scholar 

  7. Yaqub, M.F., Gondal, I., Kamruzzaman, J.: Inchoate fault detection framework: adaptive selection of wavelet nodes and cumulant orders. IEEE Trans. Instrum. Meas. 61(3), 685–695 (2012)

    Article  Google Scholar 

  8. Bouzida, A., Touhami, O., Ibtiouen, R., Belouchrani, A., Fadel, M., Rezzoug, A.: Fault diagnosis in industrial induction machines through discrete wavelet transform. IEEE Trans. Ind. Electron. 58(9), 4385–4395 (2011)

    Article  Google Scholar 

  9. Su, H., Chong, K.T.: Induction machine condition monitoring using neural network modeling. IEEE Trans. Ind. Electron. 54(1), 241–249 (2007)

    Article  Google Scholar 

  10. Bearing Data Center, http://www.eecs.case.edu/laboratory/bearing/welcome_overview.htm

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Iqbal Gondal .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Amar, M., Gondal, I., Wilson, C. (2015). Weighted ANN Input Layer for Adaptive Features Selection for Robust Fault Classification. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9490. Springer, Cham. https://doi.org/10.1007/978-3-319-26535-3_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-26535-3_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26534-6

  • Online ISBN: 978-3-319-26535-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics