Abrupt failure detection for rotor system of aero-engine based on dynamic semi-supervised learning

To detect problems for the abrupt failure of the aircraft engine rotor system, dynamic semi-supervised Learning (DSSL) algorithm is proposed based on the fuzzy k-nearest neighbour method for the dynamic evolution of the system detects. In the first phase, the labeled training data is used to initialize the classifier for FKNN learning. Then in the second phase a class evolution can be detected and be confirmed after the classification of each new pattern. In the last phase, the parameters of the evolved class are updated incrementally. Finally, the approach is illustrated feasibility and validity using the data which obtain from the rotor test stand simulation of aero-engine abrupt blade fracture failure.