Application of AE techniques for the detection of wind turbine using Hilbert-Huang transform

Abstract

This paper describes acoustic emission (AE) techniques based on Hilbert-Huang transform (HHT) that were recently exercised to characterise the AE signals released from the wind turbine bearing. Acoustic emission that detects elastic stress waves within a structure failure is capable of online monitoring and very sensitive to the fault development. AE wave is a non-stationary stochastic signal. Hilbert-Huang transform is applicable to nonlinear and non-stationary processes. With the Hilbert-Huang transform, instantaneous frequencies based on local properties of the signal can be got as functions of time and energy designated as the Hilbert spectrum that give sharp identifications of imbedded structures. We analyzed the AE signals recording from the wind turbine bearing test using Hilbert-Huang transform. The results show that the AE in the wind turbine bearing can be described in terms of features like frequency and energy, and inferences can be made about kinds of damage processes taking place in the bearing. And thus the HHT analysis method will has a good potential for the acoustic emission signal processing in the field of wind turbines.