Rule extraction for classification of acoustic emission signals using Ant Colony Optimisation

被引：35

作者：

Omkar, S. N. ^{[1
]}

Raghavendra, Karanth U. ^{[2
]}

机构：

[1] Indian Inst Sci, Dept Aerosp Engn, Bangalore 560012, Karnataka, India

[2] PES Inst Technol, Dept Comp Sci, Bangalore 560085, Karnataka, India

来源：

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE | 2008年 / 21卷 / 08期

关键词：

Acoustic emission; Swarm Intelligence; ACO; Rule extraction; Classification matrix;

D O I：

10.1016/j.engappai.2008.02.004

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Ant Colony Optimization (ACO) is used to obtain rules that can classify the data into pre-defined classes. It can be used to classify acoustic emission (AE) signals to their respective sources. ACO based technique has an advantage over conventional statistical techniques like maximum likelihood estimate, nearest neighbor classifier, etc., because they are distribution free, i.e., no knowledge is required about the distribution of data. AE test is carried Out using pulse, pencil and spark signal source on the surface of solid steel block. The signal parameters are measured using AET 5000 system. Classification of AE signal is done using Ant Colony Optimization, and the simplicity of the rules generated is emphasized. (C) 2008 Elsevier Ltd. All rights reserved.

引用

页码：1381 / 1388

页数：8

共 50 条

[31] Real-Time Source Classification with an Wavefonn Parameter Filtering of Acoustic Emission Signals [J].

Cho, Seung Hyun ;

Park, Jae Ha ;

Ahn, Bongyoung .

JOURNAL OF THE KOREAN SOCIETY FOR NONDESTRUCTIVE TESTING, 2011, 31 (02) :165-173

[32] Classification of Located Acoustic Emission Events Using Neural Network [J].

Manthei, Gerd ;

Guckert, Michael .

JOURNAL OF NONDESTRUCTIVE EVALUATION, 2023, 42 (01)

[33] Classification of the machine state in turning processes by using the acoustic emission [J].

Ocampo, Daniel Diaz ;

Aubart, Daniel ;

Gonzalez, German ;

Zanger, Frederik ;

Heizmann, Michael .

PRODUCTION ENGINEERING-RESEARCH AND DEVELOPMENT, 2024, 18 (02) :289-297

[34] Classification of the machine state in turning processes by using the acoustic emission [J].

Daniel Diaz Ocampo ;

Daniel Aubart ;

Germán González ;

Frederik Zanger ;

Michael Heizmann .

Production Engineering, 2024, 18 :289-297

[35] Classification of Located Acoustic Emission Events Using Neural Network [J].

Gerd Manthei ;

Michael Guckert .

Journal of Nondestructive Evaluation, 2023, 42

[36] Wave Mode Identification of Acoustic Emission Signals Using Phase Analysis [J].

Barroso-Romero, Maria ;

Gagar, Daniel ;

Pant, Shashank ;

Martinez, Marcias .

ACOUSTICS, 2019, 1 (02) :450-472

[37] Tool condition monitoring in honing process using acoustic emission signals [J].

Kanthababu, M. ;

Shunmugam, M. S. ;

Singaperumal, M. .

INTERNATIONAL JOURNAL OF AUTOMATION AND CONTROL, 2008, 2 (01) :99-112

[38] A criterion for evaluating the brittle fracturing of glass using acoustic emission signals [J].

V. P. Skal’skii ;

S. I. Builo ;

E. M. Stankevich .

Russian Journal of Nondestructive Testing, 2012, 48 :277-284

[39] A criterion for evaluating the brittle fracturing of glass using acoustic emission signals [J].

Skal'skii, V. P. ;

Builo, S. I. ;

Stankevich, E. M. .

RUSSIAN JOURNAL OF NONDESTRUCTIVE TESTING, 2012, 48 (05) :277-284

[40] Time-delay estimation of acoustic emission signals using ICA [J].

Kosel, T ;

Grabec, I ;

Kosel, F .

ULTRASONICS, 2002, 40 (1-8) :303-306

← 1 2 3 4 5 →