Feature Analysis Based on Acoustic Emission Signal Processing for Tubular Steel Tower Condition Monitoring

2021 ◽  
Vol 31 (2) ◽  
pp. 195-202
Author(s):  
Hyeon-Tak Yu ◽  
Tae-Hong Min ◽  
Hyeong-Jin Kim ◽  
Seoggeun Kang ◽  
Dong-Young Kang ◽  
...  
Keyword(s):  
Signal Processing ◽  
Acoustic Emission ◽  
Condition Monitoring ◽  
Acoustic Emission Signal ◽  
Feature Analysis ◽  
Emission Signal

scholarly journals Acoustic emission signal processing framework to identify fracture in aluminum alloys

2019 ◽  
Vol 210 ◽  
pp. 367-380 ◽  
Author(s):  
B. Wisner ◽  
K. Mazur ◽  
V. Perumal ◽  
K.P. Baxevanakis ◽  
L. An ◽  
...  
Keyword(s):  
Signal Processing ◽  
Acoustic Emission ◽  
Aluminum Alloys ◽  
Acoustic Emission Signal ◽  
Emission Signal ◽  
Processing Framework

Acoustic Emission Signal Analysis and Artificial Intelligence Techniques in Machine Condition Monitoring and Fault Diagnosis: A Review

2014 ◽  
Vol 69 (2) ◽  
Author(s):  
Yasir Hassan Ali ◽  
Roslan Abd Rahman ◽  
Raja Ishak Raja Hamzah
Keyword(s):  
Artificial Intelligence ◽  
Acoustic Emission ◽  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Acoustic Emission Signal ◽  
Signal Analysis ◽  
Machine Condition ◽  
Artificial Intelligence Techniques ◽  
Emission Signal ◽  
Machine Condition Monitoring

Acoustic Emission technique is a successful method in machinery condition monitoring and fault diagnosis due to its high sensitivity on locating micro cracks in high frequency domain. A recently developed method is by using artificial intelligence techniques as tools for routine maintenance. This paper presents a review of recent literature in the field of acoustic emission signal analysis through artificial intelligence in machine conditioning monitoring and fault diagnosis. Many different methods have been previously developed on the basis of intelligent systems such as artificial neural network, fuzzy logic system, Genetic Algorithms, and Support Vector Machine. However, the use of Acoustic Emission signal analysis and artificial intelligence techniques for machine condition monitoring and fault diagnosis is still rare. Although many papers have been written in area of artificial intelligence methods, this paper puts emphasis on Acoustic Emission signal analysis and limits the scope to artificial intelligence methods. In the future, the applications of artificial intelligence in machine condition monitoring and fault diagnosis still need more encouragement and attention due to the gap in the literature.

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