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
