Vibration Analysis of High-Speed Rotating Machinery

  • Authors

    • Zara Ahmed Business Development Manager, Engro Corporation, Pakistan. Author

    DOI:

    https://doi.org/10.67228/3071-6357/IJMRSE-2019PII1K8M

    Published 11-05-2019

  • Vibration Analysis, Rotating Machinery, Condition Monitoring, Fault Diagnosis, Signal Processing, Predictive Maintenance

    Issue

    Vol. 2 No. 2 (2019)

    Section

    Articles

    How to Cite

    Vibration Analysis of High-Speed Rotating Machinery. (2019). International Journal of Modern Research in Science & Engineering, 2(2), 01-16. https://doi.org/10.67228/3071-6357/IJMRSE-2019PII1K8M

  • Abstract

    The saving grace of industrial systems in the present day is high-speed rotating machinery which encompasses turbines, compressors, generators and aerospace propulsion units. The successful performance of such machines largely remains the responsibility of efficient condition monitoring and fault diagnosis methods. Vibration analysis has become one of the most potent and popular in the number of these techniques. A cohesive exploration of the vibration nature of high-speed rotating machinery with its focus on signal acquisition, signal processing, feature extraction, and fault classification techniques is discussed in this paper. The process combines both experimental measurements, mathematical modeling and using advanced signal processing to detect typical mechanical faults including imbalance, misalignment, bearing flaws, shaft cracks and gear mesh anomaly. An elaborate experimental design is crafted based on an accelerometer, data collection apparatus, and spectral analysis apparatus to record the signature of vibrations at varying operation conditions. The time-domain analysis, frequency-domain abasys and time-frequency-domain analysis are used to extract diagnostic features that are usually significant. Short-Time Fourier Transform (STFT), Fast Fourier Transform (FFT), and Wavelet Transform (WT) techniques are adopted to make a fault more detectable. In addition, automated fault recognition is performed with the help of statistical indicators and classifiers based on machine learning. The findings indicate that vibration-based diagnostics have demonstrated high relative accuracy of early fault detection and reliability of the system. Comparative study shows that the hybrid signal processing solutions are better than the conventional methods in complicated operational scenarios. The given methodology has offered a systematic framework of being predictive in maintenance developed in industrial rotating machines. The results of this study help in making the operations safe, minimizing downtime and minimizing costs of maintenance. The research can be used by the researchers and practitioners who wish to adopt modern vibration monitoring systems in the rotating machines that operate at high speed.

  • References

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