LAUSR

Cascade flutter driven by aerodynamic instability leads to severe structural destruction of turbine blades in aircraft engines. The development of a sophisticated methodology for detecting a precursor of cascade flutter is one of the most important topics in current aircraft engineering and related branches of nonlinear physics. A novel detection methodology combining symbolic dynamics, dynamical systems, and machine learning is proposed in this experimental study to detect a precursor of cascade flutter in a low-pressure turbine. Two important measures, the weighted permutation entropy in terms of symbolic dynamics and the determinism in recurrence plots in terms of dynamical systems theory, are estimated for the strain fluctuations on turbine blades to capture the significant changes in the dynamical state during a transition to cascade flutter. A feature space consisting of the two measures obtained by a support vector machine, can appropriately be classified into three dynamical states: a stable state, a transition state, and a cascade flutter state. The proposed methodology is valid for detecting a precursor of cascade flutter.