Case Study of a Faulted Planet Bearing
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Abstract
Fault detection in planet bearings is difficult. This is particularly true in wind turbines, where the main rotor shaft is under 20 rpm, such that the planet fault frequency can be sub 1 Hz. This papers analyzes a missed fault on a wind turbine planet bearing, and discuses how changes in the analysis configuration then allowed this type of fault to be detected. Raw data from ten machines was collected. From this, a strategy for fault feature identification was developed, to include: the evaluation of window selection, biasing of the data set with faulted components, and the use of improve analysis techniques. This allowed meaningful and appropriate thresholds to be set.
How to Cite
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condition monitoring, Bearing Analysis, Resampling, threshold setting
Bechhoefer, E., He, D., (2012), A Process for Data Driven Prognostics, MFPT 2012: Dayton, Ohio.
Bechhoefer, E., Duke, A., Mayhew, E. (2007). A Case for Health Indicators vs. Condition Indicators in Mechanical Diagnostics. American Helicopter Society Forum 63, Virginia Beach
Fukunaga, K., (1990), Introduction to. Statistical. Pattern Recognition, Academic Press Professional, Inc. San Diego, CA, USA,
Bechhoefer, E., He, D., Dempsey, P. (2011), Gear Threshold Setting Based On a Probability of False Alarm. Annual Conference of the Prognostics and
Health Management Society,.
Bechhoefer, E., Bernhard, A. (2007) A Generalized Process for Optimal Threshold Setting in HUMS. IEEE Aerospace Conference, Big Sky.
Antoni, J., (2009) Cyclostationarity by examples, Mechanical Systems and Signal Processing, 23, pg 987-1036.
Boskoski, P., Juricic, D. (2013), Modeling localized bearing faults using inverse Gaussian mixtures, Annual Conference of the Prognostics and Health Management Society.
Ganeriawala, S., (2006) Some Observations of the Detection of Rolling Bearing Outer Race Faults, SpectraQuest, www.spectraquest.com,
Bechhoefer, E., Van Hecke, B., and He, D., (2013), Processing for Improved Spectral Analysis, PHM Conference.
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