Development of features for blade rubbing defect classification in machine learning.

This study has developed new features necessary for condition monitoring and diagnosis of rotating machinery. These features are developed using the phase change of vibration signal, which is characteristic of blade rubbing fault. These developed features are intended to identify the fault's correct condition and severity of the rotating machinery. The difference between normal and blade rubbing fault was compared through experiments. The experimental model was produced to simulate a blade rubbing fault. The data were acquired through the experimental model and calculated using the developed features. Fault detection was confirmed by using genetic algorithm and machine learning that failure detection was possible using the developed features, it is expected that such study can evaluate the health of the rotating machinery. [ABSTRACT FROM AUTHOR]