AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Railway Sciences Article
PDF (4.8 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Research paper | Open Access

Characterization, identification and life prediction of acoustic emission signals of tensile damage for HSR gearbox housing material

Yibo Ai, Yuanyuan Zhang, Hao Cui, Weidong Zhang( )
National Center for Materials Service Safety, University of Science and Technology Beijing, Beijing, China
Show Author Information

Abstract

Purpose

This study aims to ensure the operation safety of high speed trains, it is necessary to carry out nondestructive monitoring of the tensile damage of the gearbox housing material in rail time, yet the traditional tests of mechanical property can hardly meet this requirement.

Design/methodology/approach

In this study the acoustic emission (AE) technology is applied in the tensile tests of the gearbox housing material of an high-speed rail (HSR) train, during which the acoustic signatures are acquired for parameter analysis. Afterward, the support vector machine (SVM) classifier is introduced to identify and classify the characteristic parameters extracted, on which basis the SVM is improved and the weighted support vector machine (WSVM) method is applied to effectively reduce the misidentification of the SVM classifier. Through the study of the law of relations between the characteristic values and the tensile life, a degradation model of the gearbox housing material amid tensile is built.

Findings

The results show that the growth rate of the logarithmic hit count of AE signals and that of logarithmic amplitude can well characterize the stage of the material tensile process, and the WSVM method can improve the classification accuracy of the imbalanced data to above 94%. The degradation model built can identify the damage occurred to the HSR gearbox housing material amid the tensile process and predict the service life remains.

Originality/value

The results of this study provide new concepts for the life prediction of tensile samples, and more further tests should be conducted to verify the conclusion of this research.

Keywords

Support vector machineHSR gearbox housingDamage identificationAcoustic emission technologyWeightedLife prediction

References

 

Andreykiv, O., Skalsky, V., Serhiyenko, O., & Rudavsky, D. (2010). Acoustic emission estimation of crack formation in aluminium alloys. Engineering Fracture Mechanics, 77(5), 759–767.
Crossref Google Scholar
 

Bhuiyan, M. S. H., Choudhury, I. A., Dahari, M., Nukman, Y., & Dawal, S. Z. (2016). Application of acoustic emission sensor to investigate the frequency of tool wear and plastic deformation in tool condition monitoring. Measurement, 92, 208–217.
Crossref Google Scholar
 

Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.
Crossref Google Scholar
 

Dong, X. (2007). The working principle and structural characteristics of high speed EMUs. Beijing: China Railway Publishing House (in Chinese).
 

Griffin, J. M., Shanbhag, V. V., Pereira, M. P., & Rolfe, B. F. (2021). Application of machine learning for acoustic emissions waveform to classify galling wear on sheet metal stamping tools. The International Journal of Advanced Manufacturing Technology, 116(1/2), 579–596.
Crossref Google Scholar
 

Hamdi, S. E., Le, D. A., Simon, L., Plantier, G., Sourice, A., & Feuilloy, M. (2013). Acoustic emission pattern recognition approach based on hilbert-huang transform for structural health monitoring in polymer-composite materials. Applied Acoustics, 74(5), 746–757.
Crossref Google Scholar
 

Huang, Z., Liu, Y., Mao, H., Wang, X., Li, X., & Mao, H. (2017). Feature analysis for acoustic emission signals during metal fatigue damage based on Kolmogorov entropy and correlation dimension. Journal of Vibration and Shock, 36(15), 210–214 (in Chinese).
Google Scholar
 

Li, X., Luo, G., Long, S., Chen, G., & Li, Q. (2016). Location method of acoustic emission by time reversal focusing and enhancing for steel plate. Chinese Journal of Scientific Instrument, 37(8), 1792–1799 (in Chinese).
Google Scholar
 

Melnikov, B. F., Averin, P. I., & Melnikova, E. A. (2019). Intelligent processing of acoustic emission data based on cluster analysis. Journal of Physics: Conference Series, 1236(1), 012044.
Crossref Google Scholar
 

Roder, G. J., Guerrero-mata, M. P., & Colás, R. (2003). Crack propagation in a hard-faced AISI type 304 stainless steel. Materials Characterization, 51(2/3), 95–99.
Crossref Google Scholar
 

Shi, H., Li, H., Wang, Z., & Pan, Q. (2019). Acoustic emission signal processing technology based on wavelet packet energy spectrum. Journal of Test and Measurement Technology, 33(3), 201–208 (in Chinese).
Google Scholar
 

Vapnik, V. N., & Lerner, A. Y. (1963). Recognition of patterns with help of generalized portraits. Avtomat. i Telemekh., 24(6), 774–80.
Google Scholar
 

Wang, X., Zhu, C., Mao, H., & Huang, Z. (2010). Source location of cracks in a turbine blade based on kernel principal component analysis and support vector machines. Journal of Vibration and Shock, 29(11), 226–229, 264 (in Chinese).
Google Scholar
 

Wang, W., Zang, Y., Qu, J., Li, G., & Dai, X. (2018). Dynamic stress response and fatigue reliability of gearbox housing for G-series high speed train. China Railway Science, 39(6), 90–97 (in Chinese).
Google Scholar
 

Yao, X., Zhang, Y., Ming, T., & Liu, S. (2010). Application of support vector machine and acoustic emission to fault diagnosis of gear crack. Journal of Naval University of Engineering, 22(2), 70–73, 112 (in Chinese).
Google Scholar
 
Yoshida, K., Tokuyama, Y., Yasuhara, Y., & Nishino, H. (2012). Evaluation of AE sources during tensile deformation of Al-Mg-Si alloys with different heart treatment. 30th European Conference on Acoustic Emission Testing and 7th International Conference on Acoustic Emission Granada: University of Granada.
Railway Sciences
Volume 2 Issue 2,
June 2023
Pages 225-242
DOI: 10.1108/RS-01-2023-0007
Cite this article:
Ai Y, Zhang Y, Cui H, et al. Characterization, identification and life prediction of acoustic emission signals of tensile damage for HSR gearbox housing material. Railway Sciences, 2023, 2(2): 225-242. https://doi.org/10.1108/RS-01-2023-0007

195

Views

2

Downloads

0

Crossref

Altmetrics
Received: 30 January 2023
Revised: 03 February 2023
Accepted: 03 February 2023
Published: 01 May 2023
© Ai Yibo, Zhang Yuanyuan, Cui Hao and Zhang Weidong. Published in Railway Sciences.

This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
Return