Pattern recognition of fatigue damage acoustic emission signal

Author

Qian, Wenxue ; Xie, Liyang ; Huang, Dayan ; Yin, Xiaowei

Author_Institution

Sch. of Mech. Eng. & Autom., Northeastern Univ., Shenyang, China

fYear

2009

fDate

9-12 Aug. 2009

Firstpage

4371

Lastpage

4375

Abstract

Acoustic emission (AE) of material is a common phenomenon; In fact, it has relation with the material states. In this paper, the acoustic emission test method is used and the characteristics of different phases of aluminum alloy cracks are analyzed. Artificial neural network (ANN) is used to recognize the pattern of AE of fatigue cracks. Practical test shows the above method could test the cracks that common method could not test. The result of pattern recognition is rather accurate, and is of practical engineering significance.

Keywords

acoustic emission testing; aluminium alloys; condition monitoring; fatigue cracks; mechanical engineering computing; neural nets; pattern recognition; ANN; acoustic emission signal; acoustic emission test method; aluminum alloy; artificial neural network; fatigue cracks; fatigue damage; pattern recognition; Acoustic emission; Acoustic materials; Acoustic signal detection; Acoustic testing; Acoustical engineering; Condition monitoring; Fatigue; Internal stresses; Pattern recognition; Phase change materials; Acoustic Emission; fatigue crack; pattern recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Mechatronics and Automation, 2009. ICMA 2009. International Conference on

Conference_Location

Changchun

Print_ISBN

978-1-4244-2692-8

Electronic_ISBN

978-1-4244-2693-5

Type

conf

DOI

10.1109/ICMA.2009.5246614

Filename

5246614