Research On Crack Recognition Of Fan Blade Based On Acoustic Emission

Large wind turbines are more and more widely applied in modern society. Blades are the keycomponents of the large wind turbines, the study of health monitoring to which gets more and morethe attentions of the industry. Because of its irregular shape, complex material layers and structures,hostile working conditions and so on, they are vulnerable to damage and fracture, so the healthmonitoring to large wind turbines has been a hot spot in the study of nowadays. The traits of currentmonitoring methods are low sensitivity, uneasy installation, which are not solved in the traditionalmonitoring methods. Therefore, this paper puts forward using acoustic emission technology tomonitor crack damages of wind turbine blades.This paper designs the crack models of specimen(Ⅰ type crack specimens and Ⅱtype crackspecimens) according to common damage type crack in blades, and entrusts blade manufacturers tomanufacture and process specimens. Then the author makes tensile and bending experiment threebending experiment or blade specimens by the universal testing machine. After that, the authorcollects acoustic emission signals of crack specimens by the acoustic emission signal collectingsystem which is made up of the acoustic emission sensors, preamplifiers, acquisition cards andepistatic machine interface written by Labview.After collecting signals, this paper uses MATLAB to write filter to separate noise and signalfirstly, and reuses wavelet energy spectrum coefficient method to find maximum signal energy infrequency layer, then reconstructs the signal. After that, this paper extract characteristic parameters ofdamage signals by MATLAB, and gets a conclusion that the different damage modes characteristics.After analysis, this paper gets the features of acoustic emission signals about Ⅰ, Ⅱtype crackand "1-4","2-5" material layers crack., and analyses the specimen in the damage forms and theconnection between the damage evolution process. Research shows that: through the softwarefiltering with wavelet energy spectrum coefficient method for signal filtering, the frequency range ofthe damage acoustic emission signals can be reflected accurately. The paper concludes the acousticemission signals of damage could be divided into four stages (they were matrix crack, fiber breakage,interface debonding between the fiber and matrix, and the delamination). Through the contrasting"1-4" and "2-5" two layer material of Ⅰ, Ⅱ type crack damage signals, the paper concludes crackdamage characteristics, which provides a feasible reference for fan blade health monitoring.