Physics-based Remaining Useful Life Prediction for Aircraft Engine Bearing Prognosis

Nathan Bolander, Hai Qiu, Neil Eklund, Ed Hindle, and Taylor Rosenfeld
Submission Type: 
Full Paper
AttachmentSizeTimestamp
phmc_09_41.pdf929.26 KBSeptember 17, 2009 - 8:30am

Aircraft engine bearing prognosis not only requires early detection of a bearing defect, but also the ability to predict bearing health conditions given certain operational scenarios. This paper summarizes a physics-based remaining useful life prediction method developed in the DARPA engine system prognosis (ESP) program. This investigation focuses on a typical roller bearing fault (or defect) on the outer raceway. Spall detection is based on the fusion of vibration and online oil debris sensors. Spall size estimation is derived from the amount of bearing debris chips that passed through the Oil Debris Monitor (ODM) sensor. Subscale propagation tests were performed to generate the response surface of the spall propagation rate under various operating speeds and loads. A particle filter based approach was used to track the spall propagation rate and update the prediction according to newly arrived diagnostics information. The bearing spall propagation model output, the distribution of remaining useful life, is calculated based on future operating conditions and the time the spall size crossing the failure threshold. The developed RUL prediction method was validated using full-scale bearing spall tests. The comparison of model prediction and measured ground truth demonstrated that the developed model was able to predict the spall propagation rate accurately, and its prediction accuracy and confidence can be further improved by incorporating more diagnostics updates and/or increasing the confidence in the diagnostic data.

Publication Control Number: 
041
Submission Keywords: 
aircraft engines
applications: aviation
bearings
condition monitoring
damage detection
damage modeling
damage propagation model
data driven prognostics
remaining useful life (RUL)
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