Accommodating Repair Actions into Gas Turbine Prognostics

Zakwan Skaf, Martha A Zaidan, Robert F Harrison, and Andrew R Mills
Submission Type: 
Full Paper
AttachmentSizeTimestamp
phmc_13_014.pdf1.14 MBOctober 8, 2013 - 7:02am

Elements of gas turbine degradation, such as compressor fouling, are recoverable through maintenance actions like compressor washing. These actions increase the usable engine life and optimise the performance of the gas turbine. However, these maintenance actions are performed by a separate organization to those undertaking fleet management operations, leading to uncertainty in the maintenance state of the asset. The uncertainty surrounding maintenance actions impacts prognostic efficacy.

In this paper, we adopt Bayesian on-line change point detection to detect the compressor washing events. Then, the event detection information is used as an input to a prognostic algorithm, advising an update to the estimation of remaining useful life.

To illustrate the capability of the approach, we demonstrated our on-line Bayesian change detection algorithms on synthetic and real aircraft engine service data, in order to identify the compressor washing events for a gas turbine and thus provide demonstrably improved prognosis.

Publication Year: 
2013
Publication Volume: 
4
Publication Control Number: 
014
Page Count: 
8
Submission Keywords: 
Data-driven prognostics
change detection
Bayesian inference
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
Submitted by: 
  
 
 
 

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