Skip to main content
SpringerLink
Log in
Book cover

Cyclostationarity: Theory and Methods III pp 187–198Cite as

Application of Independent Component Analysis in Temperature Data Analysis for Gearbox Fault Detection

  • Conference paper
  • First Online:

Part of the book series: Applied Condition Monitoring ((ACM,volume 6))

Abstract

In the real multisource signal analysis one of the main problems is the fact that true information is divided partially among the individual signals and/or measured signal is a mixture of different sources. This comes from the fact that input channels are typically related, and carry information about different processes occurring during the measurement. Those processes can be thought of as independent sources of vaguely understood “information”. In many cases separation and extraction of those sources can be crucial. In this paper we present the usage of Independent Component Analysis as a tool for information extraction from real-life multichannel temperature data measured on heavy duty gearboxes used in mining industry. Original signals, due to operational factors reveal cyclic variability and detection of damage was difficult. Thanks to proposed procedure, from four channels acquired from 4 gearboxes driving belt conveyor we have extracted one of 4 components, that is related to change of condition of a single gearbox. For new signal visibility of change is clear and simple automatic detection rule can be applied.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover 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. Roan, M. J., Erling, J. G., & Sibul, L. H. (2002). A new, non-linear, adaptive, blind source separation approach to gear tooth failure detection and analysis. Mechanical Systems and Signal Processing, 16(5), 719–740.

    Google Scholar 

  2. Lin, J., & Zhang, A. (2005). Fault feature separation using wavelet-ICA filter. NDT and E International, 38(6), 421–427.

    Google Scholar 

  3. Wang, J., Gao, R. X., & Yan, R. (2014). Integration of EEMD and ICA for wind turbine gearbox diagnosis. Wind Energy, 17(5), 757–773.

    Google Scholar 

  4. Wang, H., Li, R., Tang, G., Yuan, H., Zhao, Q., & Cao, X. (2014). A compound fault diagnosis for rolling bearings method based on blind source separation and ensemble empirical mode decomposition. PLoS ONE, 9(10), Article number e109166.

    Google Scholar 

  5. Antoni, J. (2015). Blind separation of vibration components: Principles and demonstrations. Mechanical Systems and Signal Processing, 19(6), 1166–1180. ISSN 0888-3270.

    Google Scholar 

  6. Cichocki, A., Zdunek, R., & Amari, S.-I. (2006). New algorithms for non-negative matrix factorization in applications to blind source separation. In 2006 IEEE International Conference on Acoustics, Speech and Signal Processing. ICASSP 2006 Proceedings (Vol. 5).

    Google Scholar 

  7. Hyvarnen, A., & Oja, E. (1997). A fast fixed-point algorithm for independent component analysis. Neural Computation, (9), 1483–1492.

    Google Scholar 

  8. Hyvarnen, A., & Oja, E. (2000). Independent component analysis: Algorithms and applications. Neural Networks, (13), 411–430.

    Google Scholar 

  9. Li, L., & Qu, L. (2002). Machine diagnosis with independent component analysis and envelope analysis. In IEEE ICIT’02 (pp. 1360–1364).

    Google Scholar 

  10. He, Q., Feng Z., & Kong, F. (2007). Detection of signal transients using independent component analysis and its application in gearbox condition monitoring. Mechanical Systems and Signal Processing, 21(5), 2056–2071.

    Google Scholar 

  11. Zuo, M. J., Lin, J., & Fan, X. (2005). Feature separation using ICA for a one-dimensional time series and its application in fault detection. Journal of Sound and Vibration, 287(3), 614–624.

    Google Scholar 

  12. Yang, W., Little, C., Tavner, P. J., & Court, R. (2014). Data-driven technique for interpreting wind turbine condition monitoring signals. IET Renewable Power Generation, 8(2), 151–159.

    Article  Google Scholar 

  13. Guo, P., Infield, D., & Yang, X. (2012). Wind turbine generator condition-monitoring using temperature trend analysis. IEEE Transactions on Sustainable Energy, 3(1), 124–133. Art. no. 5970135. doi:10.1109/TSTE.2011.2163430.

  14. Astolfi, D., Castellani, F., & Terzi, L. (2014). Fault prevention and diagnosis through SCADA temperature data analysis of an onshore wind farm. Diagnostyka, 15(2), 71–78.

    Google Scholar 

  15. Yang, W., Court, R., & Jiang, J. (2013). Wind turbine condition monitoring by the approach of SCADA data analysis. Renewable Energy, (53), 365–376.

    Google Scholar 

  16. Wilkinson, M., Darnell, B., Van Delft, T., & Harman, K. (2014). Comparison of methods for wind turbine condition monitoring with SCADA data. IET Renewable Power Generation, 8(4), 390–397.

    Google Scholar 

  17. Zhang, X., & Fan, T.-N. (2012). The research of distribute temperature monitoring system early warning fire in coal belt conveyor. Advanced Materials Research, 548, 890–892.

    Article  Google Scholar 

  18. Nembhard, A. D., Sinha, J. K., Pinkerton, A. J., & Elbhbah, K. (2013). Fault diagnosis of rotating machines using vibration and bearing temperature measurements. Diagnostyka, 14(3), 45–51.

    Google Scholar 

  19. Bongers, D. R., & Gurgenci, H. (2008). Fault detection and identification for longwall machinery using SCADA data, complex system maintenance handbook. In Springer series in reliability engineering (pp. 611–641).

    Google Scholar 

  20. Zimroz, R., Wodecki, J., Krol, R., Andrzejewski, M., Sliwinski, P., & Stefaniak, P. K. (2014). Self-propelled mining machine monitoring system—Data validation, processing and analysis. In C. Drebenstedt & R. Singhal (Eds.), Mine Planning and Equipment Selection: Proceedings of the 22nd MPES Conference (pp. 1285–1294). Dresden.

    Google Scholar 

  21. Wylomanska, A., & Zimroz, R. (2015). The analysis of stochastic signal from LHD mining machine. Springer Proceedings in Mathematics and Statistics, 122, 469–478.

    Article  Google Scholar 

  22. Zimroz, R., Madziarz, M., Żak, G., Wyłomańska, A., & Obuchowski, J. (2015). Seismic signal segmentation procedure using time-frequency decomposition and statistical modelling. Journal of Vibroengineering, 17(6), 3111–3121.

    Google Scholar 

  23. Polak, M., Obuchowski, J., Madziarz, M., Wyłomańska, A., & Zimroz, R. (2016). Time-varying group delay as a basis for clustering and segmentation of seismic signals. Journal of Vibroengineering, 18(1), 267–275.

    Google Scholar 

  24. Gajda, J., Sikora, G., & Wyłomańska, A. (2013). Regime variance testing—A quantile approach. Acta Physica Polonica B, 44(5), 1015–1035.

    Google Scholar 

  25. Makowski, R., & Hossa, R. (2014). Automatic speech signal segmentation based on the innovation adaptive filter. Journal of Applied Mathematics and Computer Science, 24(2), 259–270.

    MATH  Google Scholar 

  26. Przylibski, T., Wyłomanska, A., Zimroz, R., & Fijałkowska-Lichwa, L. (2015). Application of spectral decomposition of 222RN activity concentration signal series measured in Niedzwiedzia Cave to identification of mechanism responsible for different time-period variations. Applied Radiation and Isotopes, 104, 74–86.

    Google Scholar 

  27. Crossman, J. A., et al. (2003). Automotive signal fault diagnostics—Part I: Signal fault analysis, signal segmentation, feature extraction and quasi-optimal feature selection. IEEE Transactions on Vehicular Technology, 52(4), 1063–1075.

    Article  Google Scholar 

  28. Cempel, C., & Tabaszewski, M. (2007). Multidimensional condition monitoring of machines in non-stationary operation. Mechanical Systems and Signal Processing, 21(3), 1233–1241.

    Article  Google Scholar 

  29. Zimroz, R., & Bartkowiak, A. (2013). Two simple multivariate procedures for monitoring planetary gearboxes in non-stationary operating conditions. Mechanical Systems and Signal Processing, 38(1), 237–247.

    Article  Google Scholar 

  30. Bartkowiak, A., & Zimroz, R. (2014). Dimensionality reduction via variables selection - Linear and nonlinear approaches with application to vibration-based condition monitoring of planetary gearbox. Applied Acoustics, 77, 169–177.

    Article  Google Scholar 

  31. Zimroz, R., Bartelmus, B., Barszcz, T., & Urbanek, J. (2014). Diagnostics of bearings in presence of strong operating conditions non-stationarity—A procedure of load-dependent features processing with application to wind turbine bearings. Mechanical Systems and Signal Processing, 46(1), 16–27.

    Article  Google Scholar 

  32. Wodecki, J., Stefaniak, P., Obuchowski, J., Wyłomańska, A., & Zimroz, R. (2015). Combination of ICA and time-frequency representations of multichannel vibration data for gearbox fault detection. Vibroengineering PROCEDIA, 6, 133–138.

    Google Scholar 

  33. Sawicki, M., Zimroz, R., Wyłomańska, A., Obuchowski, J., Stefaniak, P., & Żak, G. (2015). An automatic procedure for multidimensional temperature signal analysis of a SCADA system with application to belt conveyor components. Procedia Earth and Planetary Science, 15, 781–790. ISSN 1878-5220.

    Google Scholar 

Download references

Acknowledgements

This work is supported by the Framework Programme for Research and Innovation Horizon 2020 under grant agreement n. 636834 (DISIRE—Integrated Process Control based on Distributed In Situ Sensors into Raw Material and Energy Feedstock).

Author information

Authors and Affiliations

  1. KGHM Cuprum Ltd., CBR Sikorskiego 2-8, 53-659, Wrocław, Poland

    Jacek Wodecki, Pawel Stefaniak, Mateusz Sawicki & Radoslaw Zimroz

Authors
  1. Jacek Wodecki
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pawel Stefaniak
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mateusz Sawicki
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Radoslaw Zimroz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jacek Wodecki .

Editor information

Editors and Affiliations

  1. National School of Engineers of Sfax , Sfax, Tunisia

    Fakher Chaari

  2. Institute of Mathematics, Cracow University of Technology, Cracow, Poland

    Jacek Leskow

  3. Department of Engineering, University of Napoli “Parthenope” , Napoli, Italy

    Antonio Napolitano

  4. Institute of Mining Engineering, Wrocław University of Technology, Wroclaw, Poland

    Radoslaw Zimroz

  5. Institute of Mathematics and computer Science, Wroclaw University of Technology, Wroclaw, Poland

    Agnieszka Wylomanska

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Wodecki, J., Stefaniak, P., Sawicki, M., Zimroz, R. (2017). Application of Independent Component Analysis in Temperature Data Analysis for Gearbox Fault Detection. In: Chaari, F., Leskow, J., Napolitano, A., Zimroz, R., Wylomanska, A. (eds) Cyclostationarity: Theory and Methods III. Applied Condition Monitoring, vol 6. Springer, Cham. https://doi.org/10.1007/978-3-319-51445-1_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-51445-1_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-51444-4

  • Online ISBN: 978-3-319-51445-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation