Investigation of damage identification of 16Mn steel based on artificial neural networks and data fusion techniques in tensile test

Hongwei Wang; Hongyun Luo; Zhiyuan Han; Qunpeng Zhong

doi:10.1007/978-3-642-03348-3_73

Investigation of damage identification of 16Mn steel based on artificial neural networks and data fusion techniques in tensile test

Hongwei Wang^*
, Hongyun Luo
, Zhiyuan Han
, Qunpeng Zhong

^*此作品的通讯作者

材料科学与工程学院

Beihang University

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

2 引用（Scopus）

摘要

This paper proposes a damage identification method based on back propagation neural network (BPNN) and dempster-shafer (D-S) evidence theory to analyze the acoustic emission (AE) data of 16Mn steel in tensile test. Firstly, the AE feature parameters of each sensor in 16Mn steel tensile test are extracted. Secondly, BPNNs matching sensor number are trained and tested by the selected features of the AE data, and the initial damage decision is made by each BPNN. Lastly, the outputs of each BPNN are combined by D-S evidence theory to obtain the finally damage identification of 16Mn steel in tensile test. The experimental results show that the damage identification method based on BPNN and D-S evidence theory can improve damage identification accuracy in comparison with BPNN alone and decrease the effect of the environment noise.

源语言	英语
主期刊名	Advanced Data Mining and Applications - 5th International Conference, ADMA 2009, Proceedings
页	696-703
页数	8
DOI	https://doi.org/10.1007/978-3-642-03348-3_73
出版状态	已出版 - 2009
活动	5th International Conference on Advanced Data Mining and Applications, ADMA 2009 - Beijing, 中国期限: 17 8月 2009 → 19 8月 2009

出版系列

姓名	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
卷	5678 LNAI
ISSN（印刷版）	0302-9743
ISSN（电子版）	1611-3349

会议

会议	5th International Conference on Advanced Data Mining and Applications, ADMA 2009
国家/地区	中国
市	Beijing
时期	17/08/09 → 19/08/09

访问文件

10.1007/978-3-642-03348-3_73

其它文件与链接

链接到 Scopus 的出版物

引用此

Wang, H., Luo, H., Han, Z., & Zhong, Q. (2009). Investigation of damage identification of 16Mn steel based on artificial neural networks and data fusion techniques in tensile test. 在 Advanced Data Mining and Applications - 5th International Conference, ADMA 2009, Proceedings (页码 696-703). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 卷 5678 LNAI). https://doi.org/10.1007/978-3-642-03348-3_73

Wang, Hongwei ; Luo, Hongyun ; Han, Zhiyuan 等. / Investigation of damage identification of 16Mn steel based on artificial neural networks and data fusion techniques in tensile test. Advanced Data Mining and Applications - 5th International Conference, ADMA 2009, Proceedings. 2009. 页码 696-703 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{8582e97fd8a842f3be9d1ae644644ca6,

title = "Investigation of damage identification of 16Mn steel based on artificial neural networks and data fusion techniques in tensile test",

abstract = "This paper proposes a damage identification method based on back propagation neural network (BPNN) and dempster-shafer (D-S) evidence theory to analyze the acoustic emission (AE) data of 16Mn steel in tensile test. Firstly, the AE feature parameters of each sensor in 16Mn steel tensile test are extracted. Secondly, BPNNs matching sensor number are trained and tested by the selected features of the AE data, and the initial damage decision is made by each BPNN. Lastly, the outputs of each BPNN are combined by D-S evidence theory to obtain the finally damage identification of 16Mn steel in tensile test. The experimental results show that the damage identification method based on BPNN and D-S evidence theory can improve damage identification accuracy in comparison with BPNN alone and decrease the effect of the environment noise.",

keywords = "Acoustic emission, Back propagation neural network, Damage identification, Dempster-shafer evidence theory",

author = "Hongwei Wang and Hongyun Luo and Zhiyuan Han and Qunpeng Zhong",

year = "2009",

doi = "10.1007/978-3-642-03348-3\_73",

language = "英语",

isbn = "3642033474",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "696--703",

booktitle = "Advanced Data Mining and Applications - 5th International Conference, ADMA 2009, Proceedings",

note = "5th International Conference on Advanced Data Mining and Applications, ADMA 2009 ; Conference date: 17-08-2009 Through 19-08-2009",

}

Wang, H, Luo, H, Han, Z & Zhong, Q 2009, Investigation of damage identification of 16Mn steel based on artificial neural networks and data fusion techniques in tensile test. 在 Advanced Data Mining and Applications - 5th International Conference, ADMA 2009, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 卷 5678 LNAI, 页码 696-703, 5th International Conference on Advanced Data Mining and Applications, ADMA 2009, Beijing, 中国, 17/08/09. https://doi.org/10.1007/978-3-642-03348-3_73

Investigation of damage identification of 16Mn steel based on artificial neural networks and data fusion techniques in tensile test. / Wang, Hongwei; Luo, Hongyun; Han, Zhiyuan 等.
Advanced Data Mining and Applications - 5th International Conference, ADMA 2009, Proceedings. 2009. 页码 696-703 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 卷 5678 LNAI).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - Investigation of damage identification of 16Mn steel based on artificial neural networks and data fusion techniques in tensile test

AU - Wang, Hongwei

AU - Luo, Hongyun

AU - Han, Zhiyuan

AU - Zhong, Qunpeng

PY - 2009

Y1 - 2009

N2 - This paper proposes a damage identification method based on back propagation neural network (BPNN) and dempster-shafer (D-S) evidence theory to analyze the acoustic emission (AE) data of 16Mn steel in tensile test. Firstly, the AE feature parameters of each sensor in 16Mn steel tensile test are extracted. Secondly, BPNNs matching sensor number are trained and tested by the selected features of the AE data, and the initial damage decision is made by each BPNN. Lastly, the outputs of each BPNN are combined by D-S evidence theory to obtain the finally damage identification of 16Mn steel in tensile test. The experimental results show that the damage identification method based on BPNN and D-S evidence theory can improve damage identification accuracy in comparison with BPNN alone and decrease the effect of the environment noise.

AB - This paper proposes a damage identification method based on back propagation neural network (BPNN) and dempster-shafer (D-S) evidence theory to analyze the acoustic emission (AE) data of 16Mn steel in tensile test. Firstly, the AE feature parameters of each sensor in 16Mn steel tensile test are extracted. Secondly, BPNNs matching sensor number are trained and tested by the selected features of the AE data, and the initial damage decision is made by each BPNN. Lastly, the outputs of each BPNN are combined by D-S evidence theory to obtain the finally damage identification of 16Mn steel in tensile test. The experimental results show that the damage identification method based on BPNN and D-S evidence theory can improve damage identification accuracy in comparison with BPNN alone and decrease the effect of the environment noise.

KW - Acoustic emission

KW - Back propagation neural network

KW - Damage identification

KW - Dempster-shafer evidence theory

UR - https://www.scopus.com/pages/publications/70350340334

U2 - 10.1007/978-3-642-03348-3_73

DO - 10.1007/978-3-642-03348-3_73

M3 - 会议稿件

AN - SCOPUS:70350340334

SN - 3642033474

SN - 9783642033476

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 696

EP - 703

BT - Advanced Data Mining and Applications - 5th International Conference, ADMA 2009, Proceedings

T2 - 5th International Conference on Advanced Data Mining and Applications, ADMA 2009

Y2 - 17 August 2009 through 19 August 2009

ER -

Wang H, Luo H, Han Z, Zhong Q. Investigation of damage identification of 16Mn steel based on artificial neural networks and data fusion techniques in tensile test. 在 Advanced Data Mining and Applications - 5th International Conference, ADMA 2009, Proceedings. 2009. 页码 696-703. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-03348-3_73

Investigation of damage identification of 16Mn steel based on artificial neural networks and data fusion techniques in tensile test

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