State Identification of ECS Turbine Bearing Based on Fractal Dimension

Abstract EN

According to nonlinear characteristics of vibration signals measured on the turbine used in the aircraft environment control system (ECS), the ensemble empirical mode decomposition (EEMD) together with fractal dimension analysis is investigated in the paper to extract characteristic quantities for the goal of fault diagnosis of turbine bearings.

Firstly, in order to filter noise signal vibration and advance signal-to-noise signals under different statements of bearings, including normal bearing, inner ring fault, outer ring fault, and cage fault, are decomposed by EEMD.

Then correlation dimension of those signals phase is calculated, contrasted, and analyzed after space reconstruction.

The experimental result shows that the correlation dimension, as nonlinear geometric invariants, can be used as the characteristic quantity of ECS turbine bearing on running state.

Moreover, this method can accurately and effectively identify the running state of the bearing.