Accommodating Repair Actions into Gas Turbine Prognostics

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Zakwan Skaf Martha A Zaidan Robert F Harrison Andrew R Mills

Abstract

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 significant 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.

How to Cite

Skaf, Z. ., A Zaidan, M. ., F Harrison, R. ., & R Mills, A. . (2013). Accommodating Repair Actions into Gas Turbine Prognostics. Annual Conference of the PHM Society, 5(1). https://doi.org/10.36001/phmconf.2013.v5i1.2223
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Keywords

Bayesian inference, Data-driven prognostics, change detection

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Section
Technical Papers