论文信息 - Detecting and identifying artificial acoustic emission signals in an industrial fatigue environment

Detecting and identifying artificial acoustic emission signals in an industrial fatigue environment

This paper details progress towards the application of a methodology for Acoustic Emission (AE) detection and interpretation for the monitoring of fatigue fractures in large-scale industrial environments. An artificial acoustic emission source, representative of a fatigue fracture was injected into a test of a substantial landing gear component. An AE monitoring system was then used to successfully locate and identify the source using the new signal processing methodology.

[1] Hichem Frigui,et al. Self-Organization of Pulse-Coupled Oscillators with Application to Clustering , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[2] Karen Margaret Holford,et al. Modal Analysis of Acoustic Emission Signals from Artificial and Fatigue Crack Sources in Aerospace Grade Steel , 2005 .

[3] Heekuck Oh,et al. Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[4] C. B. Scruby,et al. Characterisation of fatigue crack extension by quantitative acoustic emission , 1985, International Journal of Fracture.

[5] P. J. Green,et al. Density Estimation for Statistics and Data Analysis , 1987 .

[6] Keith Worden,et al. Detecting and identifying artificial acoustic emission signals in an industrial fatigue environment , 2009 .

[7] Laurie J. Heyer,et al. Exploring expression data: identification and analysis of coexpressed genes. , 1999, Genome research.

[8] John A. Nelder,et al. A Simplex Method for Function Minimization , 1965, Comput. J..