Blade Tip Timing (BTT) has been in existence for many decades as an attractive vibration based
condition monitoring technique for turbomachine blades. The technique is non-intrusive and online
monitoring is possible. For these reasons, BTT may be regarded as a feasible technique to track
the condition of turbomachine blades, thus preventing unexpected and catastrophic failures. The
processing of BTT data to find the associated vibration characteristics is however non-trivial. In
addition, these vibration characteristics are difficult to validate, therefore resulting in great uncertainty
of the reliability of BTT techniques. This article therefore proposes a hybrid approach comprising a
stochastic Finite Element Model (FEM) based modal analysis and Bayesian Linear Regression (BLR)
based BTT technique. The use of this stochastic hybrid approach is demonstrated for the identification
and classification of turbomachine blade damage. For the purposes of this demonstration, discrete
damage is incrementally introduced to a simplified test blade of an experimental rotor setup. The
damage identification and classification processes are further used to determine whether a damage
threshold has been reached, therefore providing sufficient evidence to schedule a turbomachine outage.
It is shown that the proposed stochastic hybrid approach may offer many short- and long-term benefits
for practical implementation.
Dissertation (MEng)--University of Pretoria, 2017.