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Efficient Methodology for Estimation of Vibration Thresholds for Electrical Machines

  • D. Ganga
  • V. Ramachandran
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 748)

Abstract

This paper discusses about deducing the impacts of dynamic operating conditions of the DC machine on the shaft vibration pattern by proposing a statistical classification based signal processing technique, which estimates the signal oscillations at multiple amplitude levels. This analysis parameterizes the vibration signal with the oscillation information so that the non-stationary amplitude pattern of the vibration has been extracted efficiently which enables effective fixation of vibration thresholds for safe and reliable operation of machines in industrial environments. The variations in the pattern of the non-stationary vibration signal have been identified by using the signal transition matrix. The proposed technique determines the vibration thresholds at different machine operating conditions from the decomposed signal oscillations by clustering and enumerating the set of denser oscillation levels as characterized levels of vibration which distinguish the incipient changes in the machine conditions efficiently. The technique on implementation has traced the dynamic changes in the real-time rotor shaft vibration of DC shunt motor during starting from stall condition and sudden load changes with and without external disturbances. The efficiency of the technique has been outlined by comparing its performance with the outcomes of widely adopted non-stationary signal feature extraction methods of Joint Time-Frequency Analysis (JTFA) such as Short Time Fourier Transform and Gabor Transform, executed with different settings of the parameters namely type of window, window length and time steps. The effectiveness of the proposed technique in extracting the variations of the shaft vibration in a simple, faster and detailed manner has been elucidated through comparative analysis.

Keywords

Vibration threshold Electrical machines Time-frequency analysis Signal processing 

References

  1. 1.
    Vishwakarma, M., Purohit, R., Harshlata, V., Rajput, P.: Vibration analysis & condition monitoring for rotating machines: a review. In: Materials Today: Proceedings of 5th International Conference of Materials Processing and Characterization (ICMPC 2016), pp. 2659–2664. Elsevier Ltd. (2017)Google Scholar
  2. 2.
    Al-Badour, F., Sunar, M., Cheded, L.: Vibration analysis of rotating machinery using time–frequency analysis and wavelet techniques. Mech. Syst. Signal Process. 25, 2083–2101 (2011)CrossRefGoogle Scholar
  3. 3.
    Galar, D., Sandborn, P., Kumar, U., Johansson, C.-A.: SMART: integrating human safety risk assessment with asset integrity. In: Dalpiaz, G., D’Elia, G., Rubini, R., Cocconcelli, M., Chaari, F., Haddar, M., Zimroz, R., Bartelmus, W. (eds.) Proceedings of the Third International Conference on Condition Monitoring of Machinery in Non-Stationary Operations CMMNO 2013, LNME, vol. 7, pp. 37–59. Springer, Heidelberg (2014)Google Scholar
  4. 4.
    Straczkiewicz, M., Barszcz, T., Jablonski, A.: Detection and classification of alarm threshold violations in condition monitoring systems working in highly varying operational conditions. J. Phys: Conf. Ser. 628, 1–8 (2015)Google Scholar
  5. 5.
    Cardona-Morales, O., Alvarez-Marin, D., Castellanos-Dominguez, G.: Condition monitoring under non-stationary operating conditions using time–frequency representation-based dynamic features. In: Dalpiaz, G., D’Elia, G., Rubini, R., Cocconcelli, M., Chaari, F., Haddar, M., Zimroz, R., Bartelmus, W. (eds.) Proceedings of the Third International Conference on Condition Monitoring of Machinery in Non-Stationary Operations CMMNO 2013, LNME, vol. 7, pp. 441–451. Springer, Heidelberg (2014)Google Scholar
  6. 6.
    Zhu, J., Nostrand, T., Spiegel, C., Morton, B.: Survey of condition indicators for condition monitoring systems. In: Proceedings of Annual Conference of the Prognostics and Health Management Society, pp. 1–13 (2014)Google Scholar
  7. 7.
    Prudhom, A., Antonino-Daviu, J., Razik, H., Climente-Alarcon, V.: Time-frequency vibration analysis for the detection of motor damages caused by bearing currents. Mech. Syst. Signal Process. 84 Part A, 747–762 (2017)Google Scholar
  8. 8.
    Feng, Z., Liang, M., Chu, F.: Recent advances in time–frequency analysis methods for machinery fault diagnosis: a review with application examples. Mech. Syst. Signal Process. 38, 165–205 (2013)CrossRefGoogle Scholar
  9. 9.
    Jablonski, A., Barszcz, T., Bielecka, M., Breuhaus, P.: Modeling of probability distribution functions for automatic threshold calculation in condition monitoring systems. Measurement 46, 727–738 (2013)CrossRefGoogle Scholar
  10. 10.
  11. 11.
  12. 12.
    National Instruments: LabVIEW-Joint Time-Frequency Analysis Toolkit Reference Manual, Part Number 320544D-01 (1998)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.NIT NagalandChumukedima, DimapurIndia
  2. 2.College of Engineering Guindy, Anna UniversityChennaiIndia

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