Equipment Condition Identification Based on Telemetry Signal Clustering

  • Alexander Eroma
  • Andrei Dukhounik
  • Oleg Aksenov
  • Yauheni MarushkoEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1055)


This paper deals with the problem of pattern detection in telemetry data, in particular, the approach of automatic machine state detection based on the vibration signal proposed. The approach based on the analysis of the signal via clustering. The paper provides basic information about telemetry data analysis, vibration data analysis, and machine condition monitoring. Also, an overview of cluster analysis methods provided. The proposed approach based on clustering of objects represented with feature set extracted from vibration signals. Given the explanation of the technique and illustrative example of the application of the proposed approach applied to vibration data provided by SmartEdge Agile device for industrial electric motor considered.


Signal processing Vibration signals Clustering Unsupervised learning Predictive maintenance 


  1. 1.
    Your AI journey with Brainium and SmartEdge Agile Accessed 25 Feb 2019
  2. 2.
    LSM6DSL official documentation. Accessed 21 Feb 2019
  3. 3.
    Vibration Analysis: FFT, PSD, and Spectrogram Basics. Accessed 25 Feb 2019
  4. 4.
    Jung, D., Zhang, Z., Winslett, M.: Vibration analysis for IoT enabled predictive maintenance. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE) (2017).
  5. 5.
    Selcuk, S.: Predictive maintenance, its implementation and latest trends. Proc. Inst. Mech. Eng. Part B: J. Eng. Manuf. 231(9), 1670–1679 (2016). Scholar
  6. 6.
    Schwabacher, M.: A Survey of Data-Driven Prognostics. Infotech@Aerospace (2005).
  7. 7.
    Miljković, D.: Novelty detection in machine vibration data based on cluster intraset distance (2016)Google Scholar
  8. 8.
    Mosallam, A., Medjaher, K., Zerhouni, N.: Time series trending for condition assessment and prognostics. J. Manuf. Technol. Manag. 25(4), 550–567 (2014). Scholar
  9. 9.
    Amruthnath, N., Gupta, T.: Fault class prediction in unsupervised learning using model-based clustering approach. In: 2018 International Conference on Information and Computer Technologies (ICICT) (2018).
  10. 10.
    Betta, G., Liguori, C., Paolillo, A., Pietrosanto, A.: A DSP-based FFT-analyzer for the fault diagnosis of rotating machine based on vibration analysis. IEEE Trans. Instrum. Meas. 51(6), 1316–1322 (2002). Scholar
  11. 11.
    Al-Badour, F., Sunar, M., Cheded, L.: Vibration analysis of rotating machinery using time–frequency analysis and wavelet techniques. Mech. Syst. Signal Process. 25(6), 2083–2101 (2011). Scholar
  12. 12.
    Ayvazyan, S.A., Buchstaber, V.M., Enyukov, I.S., Meshalkin, L.D.: Applied statistics: classification and dimension reduction. In: Ayvazian, S.A. (ed.) Finance and Statistics, 607 p. (1989)Google Scholar
  13. 13.
    Viatchenin, D.A.: Fuzzy methods of automatic classification, 219 p. Technoprint, Minsk (2004)Google Scholar
  14. 14.
    Kotel’nikov, V.A.: On the carrying capacity of the “ether” and wire in telecommunications. In: Material for the First All-Union Conference on Questions of Communication. Izd. Red. Upr. Svyazi RKKA, Moscow, Russian (1933)Google Scholar
  15. 15.
    Smirnova, V. (ed.): Basis of vibration measurement. According to the materials of the company DLI. Accessed 21 Feb 2019
  16. 16.
    Pasinetti, L.L.: Structural Change and Economic Growth, Chap. 11. Cambridge University Press, CambridgeGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Alexander Eroma
    • 1
    • 2
  • Andrei Dukhounik
    • 1
    • 2
  • Oleg Aksenov
    • 1
    • 2
  • Yauheni Marushko
    • 3
    Email author
  1. 1.Octonion TechnologyMinskBelarus
  2. 2.Belarusian State University of Informatics and RadioelectronicsMinskBelarus
  3. 3.United Institute of Informatics Problems, National Academy of Sciences of BelarusMinskBelarus

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