Skip to main content
SpringerLink
Log in

Applying Big Data Intelligence for Real Time Machine Fault Prediction

  • Conference paper
  • First Online:
Big Data Analytics (BDA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11297))

Included in the following conference series:

  • 1508 Accesses

Abstract

Continuous use of mechanical systems requires precise maintenance. Automatic monitoring of such systems generates a large amount of data which require intelligent mining methods for processing and information extraction. The problem is to predict the faults generated with ball bearing which severely degrade operating conditions of machinery. We develop a distributed fault prediction model based on big data intelligence that extracts nine essential features from ball bearing dataset through distributed random forest. We also perform a rigorous simulation analysis of the proposed approach and the results ensure the accuracy/correctness of the method. Different types of fault classes are considered for prediction purpose and classification is done in a supervised distributed environment.

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

Access this chapter

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 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

Institutional subscriptions

References

  1. Lacey, S.: The role of vibration monitoring in predictive maintenance. Asset Manag. Maintenance J. 24(1), 42 (2011)

    Google Scholar 

  2. Widodo, A., Yang, B.S., Han, T.: Combination of independent component analysis and support vector machines for intelligent faults diagnosis of induction motors. Expert Syst. Appl. 32(2), 299–312 (2007)

    Article  Google Scholar 

  3. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  Google Scholar 

  4. Kuang, L., Hao, F., Yang, L.T., Lin, M., Luo, C., Min, G.: A tensor-based approach for big data representation and dimensionality reduction. IEEE Trans. Emerg. Top. Comput. 2(3), 280–291 (2014)

    Article  Google Scholar 

  5. Apache Hadoop website (2017). http://hadoop.apache.org/. Accessed 07 Dec 2017

  6. Apache Spark website (2017). https://spark.apache.org/. Accessed 07 Dec 2017

  7. Zaharia, M.: Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing. In: Proceedings of the 9th USENIX Conference on Networked Systems Design and Implementation, p. 2. USENIX Association, April 2012

    Google Scholar 

  8. Othman, M.S., Nuawi, M.Z., Mohamed, R.: Vibration and Acoustic Emission Signal Monitoring for Detection of Induction Motor Bearing Fault

    Google Scholar 

  9. Eren, L., Devaney, M.J.: Bearing damage detection via wavelet packet decomposition of the stator current. IEEE Trans. Instrum. Meas. 53(2), 431–436 (2004)

    Article  Google Scholar 

  10. Devaney, M.J., Eren, L.: Detecting motor bearing faults. IEEE Instrum. Meas. Mag. 7(4), 30–50 (2004)

    Article  Google Scholar 

  11. Mendel, E., et al.: Automatic bearing fault pattern recognition using vibration signal analysis. In: IEEE International Symposium on Industrial Electronics, ISIE 2008, pp. 955–960. IEEE, June 2008

    Google Scholar 

  12. Rashid, M.M., Amar, M., Gondal, I., Kamruzzaman, J.: A data mining approach for machine fault diagnosis based on associated frequency patterns. Appl. Intell. 45(3), 638–651 (2016)

    Article  Google Scholar 

  13. Thelaidjia, T., Moussaoui, A., Chenikher, S.: Feature extraction and optimized support vector machine for severity fault diagnosis in ball bearing. Eng. Solid Mech. 4(4), 167–176 (2016)

    Article  Google Scholar 

  14. Al-Raheem, K.F., Abdul-Karem, W.: Rolling bearing fault diagnostics using artificial neural networks based on Laplace wavelet analysis. Int. J. Eng. Sci. Technol. 2(6) (2010)

    Google Scholar 

  15. Peng, H.W., Chiang, P.J.: Control of mechatronics systems: ball bearing fault diagnosis using machine learning techniques. In: 2011 8th Asian Control Conference (ASCC), pp. 175–180. IEEE, May 2011

    Google Scholar 

  16. Yang, B.S., Di, X., Han, T.: Random forests classifier for machine fault diagnosis. J. Mech. Sci. Technol. 22(9), 1716–1725 (2008)

    Article  Google Scholar 

  17. Abdulsalam, H., Skillicorn, D.B., Martin, P.: Classification using streaming random forests. IEEE Trans. Knowl. Data Eng. 23(1), 22–36 (2011)

    Article  Google Scholar 

  18. Bharathidason, S., Venkataeswaran, C.J.: Improving classification accuracy based on random forest model with uncorrelated high performing trees. Int. J. Comput. Appl 101(13), 26–30 (2014)

    Google Scholar 

  19. Bernard, S., Adam, S., Heutte, L.: Dynamic random forests. Pattern Recogn. Lett. 33(12), 1580–1586 (2012)

    Article  Google Scholar 

  20. Murdopo, A.: Distributed decision tree learning for mining big data streams. Master of Science Thesis, European Master in Distributed Computing (2013)

    Google Scholar 

  21. Dai, W., Ji, W.: A mapreduce implementation of C4. 5 decision tree algorithm. Int. J. Database Theor. Appl. 7(1), 49–60 (2014)

    Article  Google Scholar 

  22. Wang, S., Zhai, J., Zhang, S., Zhu, H.: An ordinal random forest and its parallel implementation with mapreduce. In: 2015 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 2170–2173. IEEE, October 2015

    Google Scholar 

  23. Han, J., Liu, Y., Sun, X.: A scalable random forest algorithm based on mapreduce. In: 2013 4th IEEE International Conference on Software Engineering and Service Science (ICSESS), pp. 849–852. IEEE, May 2013

    Google Scholar 

  24. Bhagat, R.C., Patil, S.S.: Enhanced SMOTE algorithm for classification of imbalanced big-data using random forest. In: 2015 IEEE International Advance Computing Conference (IACC), pp. 403–408. IEEE, June 2015

    Google Scholar 

  25. Zhang, C., Yuan, D.: Fast fine-grained air quality index level prediction using random forest algorithm on cluster computing of spark. In: 2015 IEEE 12th International Conference on Ubiquitous Intelligence and Computing and 2015 IEEE 12th International Conference on Autonomic and Trusted Computing and 2015 IEEE 15th International Conference on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom), pp. 929–934. IEEE, August 2015

    Google Scholar 

  26. Murphy, K.P.: Machine Learning: A Probabilistic Perspective. MIT press, Cambridge (2012)

    MATH  Google Scholar 

  27. Qiu, H., Lee, J., Lin, J., Yu, G.: Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics. J. Sound Vib. 289(4), 1066–1090 (2006)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Indian Institute of Information Technology Allahabad, Allahabad, 211015, UP, India

    Amrit Pal & Manish Kumar

Authors
  1. Amrit Pal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Manish Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amrit Pal .

Editor information

Editors and Affiliations

  1. Ashoka University, Sonepat, India

    Anirban Mondal

  2. IBM Research - India, New Delhi, India

    Himanshu Gupta

  3. University of Minnesota, Minneapolis, MN, USA

    Jaideep Srivastava

  4. IIIT, Hyderabad, India

    P. Krishna Reddy

  5. National Institute of Technology, Warangal, India

    D.V.L.N. Somayajulu

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Pal, A., Kumar, M. (2018). Applying Big Data Intelligence for Real Time Machine Fault Prediction. In: Mondal, A., Gupta, H., Srivastava, J., Reddy, P., Somayajulu, D. (eds) Big Data Analytics. BDA 2018. Lecture Notes in Computer Science(), vol 11297. Springer, Cham. https://doi.org/10.1007/978-3-030-04780-1_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-04780-1_26

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04779-5

  • Online ISBN: 978-3-030-04780-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation