A Comparison of Acoustic Emission and Vibration Measurements for Condition Monitoring of an Offshore Drilling Machine

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Published Oct 2, 2017
Martin Hemmer Tor I. Waag

Abstract

This paper investigates the application of heterodyned Acoustic Emission (AE) compared to more conventional vibration measurements for Condition Monitoring (CM) of an offshore drilling machine, with a particular focus on the large, axial tapered roller bearing supporting the drill string weight in a top drive. The focus on cost reduction and operational uptime in the oil and gas industry motivates research on improved CM methods for fault detection, identification and ultimately prediction. However, bearing failure on this type of machines are currently responsible for a significant share of operational downtime on drilling rigs. In the experiment, a previously used and replaced bearing is compared to a new, healthy bearing with the purpose of identifying possible condition indicators (CI) from the vibration and AE measurements. AE root-mean-square values (RMS) was identified as a CI, being more consistent with the expected bearing health than vibration measurements and also less affected by operating speed. The AE measurements also show complementary forced frequency identification capabilities compared to the vibration measurements. The particular failure mode with bearing roller end damage is described and seen in conjunction with the results.

How to Cite

Hemmer, M., & Waag, T. I. (2017). A Comparison of Acoustic Emission and Vibration Measurements for Condition Monitoring of an Offshore Drilling Machine. Annual Conference of the PHM Society, 9(1). https://doi.org/10.36001/phmconf.2017.v9i1.2430
Abstract 199 | PDF Downloads 933

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Keywords

bearings, condition monitoring, acoustic emission, vibration, Offshore drilling machine

References
Antoni, J. (2007a). Cyclic spectral analysis of rollingelement bearing signals: Facts and fictions. Journal of Sound and Vibration, 304(3-5), 497–529. doi:
10.1016/j.jsv.2007.02.029
Antoni, J. (2007b). Fast computation of the kurtogram for the detection of transient faults. Mechanical Systems and Signal Processing, 21, 108–124. doi: 10.1016/j.ymssp.2005.12.002
Antoni, J. (2009). Cyclostationarity by examples (Vol. 23) (No. 4). doi: 10.1016/j.ymssp.2008.10.010
Bechhoefer, E., Schlanbusch, R., &Waag, T. I. (2016). Techniques for Large, Slow Bearing Fault Detection. International Journal of Prognostics and Health Management, 7(1), 1–12.
He, M., He, D., & Bechhoefer, E. (2016). Using Deep Learning Based Approaches for Bearing Fault Diagnosis with AE Sensors. In Annual conference of the prognostics and health management society (pp. 1–10).
Hecke, B. V., Yoon, J., & He, D. (2016). Low speed bearing fault diagnosis using acoustic emission sensors. APPLIED ACOUSTICS, 105, 35–44. doi:
10.1016/j.apacoust.2015.10.028
Jeffrey, L. (2012). Noble 2012 Analyst & Investor Day presentation.
Kilundu, B., Chiementin, X., Duez, J., & Mba, D. (2011). Cyclostationarity of Acoustic Emissions (AE) for monitoring bearing defects. Mechanical
Systems and Signal Processing, 2061–2072. doi: 10.1016/j.ymssp.2011.01.020
Qu, Y., Bechhoefer, E., He, D., & Zhu, J. (2013). A New Acoustic Emission Sensor Based Gear Fault Detection Approach. International Journal of Prognostics and Health Management, 4, 1–14.
Randall, R. B., & Antoni, J. (2011). Rolling element bearing diagnostics-A tutorial. Mechanical Systems and Signal Processing, 25(2), 485–520. doi:
10.1016/j.ymssp.2010.07.017
Yoshioka, T., & Fujiwara, T. (1982). A new acoustic emission source locating system for the study of rolling contact fatigue. Wear, 81(1), 183–186.
Yoshioka, T., & Fujiwara, T. (1984). Application of acoustic emission technique to detection of rolling bearing failure. American society of mechanical engineers, 14(1), 55–76.
Section
Technical Research Papers