blade faults Research Papers - Academia.edu (original) (raw)

Abstract. In the recent years, the clearance between the rotor blades and stator/casing had been
getting smaller and smaller prior improving the aerodynamic efficiency of the turbomachines as
demand in the engineering field. Due to the clearance reduction between the blade tip and the
rotor casing and between rotor blades and stator blades, axial and radial blade rubbing could be
occurred, especially at high speed resulting into complex nonlinear vibrations. The primary aim
of this study is to address the blade axial rubbing phenomenon using numerical analysis of rotor
system. A comparison between rubbing caused impacts of axial and radial blade rubbing and
rubbing forces are also aims of this study. Tow rotor models (rotor-stator and rotor casing
models) has been designed and sketched using SOILDSWORKS software. ANSYS software has
been used for the simulation and the numerical analysis. The rubbing conditions were simulated
at speed range of 1000rpm, 1500rpm and 2000rpm. Analysis results for axial blade rubbing
showed the appearance of blade passing frequency and its multiple frequencies (1x, 2x 3x etc.)
and these frequencies will more excited with increasing the rotational speed. Also, it has been
observed that when the rotating speed increased, the rubbing force and the harmonics frequencies
in x, y and z-direction become higher and severe. The comparison study showed that axial blade
rub is more dangerous and would generate a higher vibration impacts and higher blade rubbing
force than radial blade rub.

- by
- •
- Rotordynamics, Turbine blades, blade faults

In this paper, the artificial neural network (ANN) has been utilized for rotating machinery faults detection and classification. First, experiments were performed to measure the lateral vibration signals of laboratory test rigs for rotor-disk-blade when the blades are defective. A rotor-disk-blade system with 6 regular blades and 5 blades with various defects was constructed. Second, the ANN was applied to classify the different x-and y-axis lateral vibrations due to different blade faults. The results based on training and testing with different data samples of the fault types indicate that the ANN is robust and can effectively identify and distinguish different blade faults caused by lateral vibrations in a rotor. As compared to the literature, the present paper presents a novel work of identifying and classifying various rotating blade faults commonly encountered in rotating machines using ANN. Experimental data of lateral vibrations of the rotor-disk-blade system in both x-and y-directions are used for the training and testing of the network.