The Identification of Engine Noise Source Based on Simulink Blind Sources Separation

被引:0
作者
Gao, Junwen [1 ,2 ,3 ]
Quan, Yanming [1 ]
Li, Yongman [4 ]
机构
[1] SCUT, Sch Mech & Automot Engn, Guangzhou 510640, Peoples R China
[2] Business Polytech Coll, Guangzhou 510507, Guangdong, Peoples R China
[3] Guangdong Agr Ind, Guangzhou 510507, Guangdong, Peoples R China
[4] Chinese Acad Sci, Guangzhou Inst Energy Convers, Guangzhou 510640, Peoples R China
来源
MECHATRONICS AND MATERIALS PROCESSING I, PTS 1-3 | 2011年 / 328-330卷
关键词
Engine noise source; Blind source separation; Mixed signal; Simulink;
D O I
10.4028/www.scientific.net/AMR.328-330.2134
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
AC algorithm of blind sources has been applied to the identification of engine noise sources. However, the testing platform based on the blind separation theory is difficult to actualize some experiments and obtain data in the real environment, or to obtain these requires high cost. Therefore, this paper proposes a new approach to construct an experimental testing platform-Sinmulink Model so as to identify engine surface noise sources. This model is applied to the test of the experimental data. These experiments demonstrate that Simulink model is efficient to identify engine noise source and the testing data accord with that made by the former platform. It is obvious that Simulink model, to some degree, can be used to replace engine surface noise sources testing platform.
引用
收藏
页码:2134 / +
页数:2
相关论文
共 50 条
  • [21] Overcomplete blind source separation of finite alphabet sources
    Ihm, BC
    Park, DJ
    Kwon, YH
    [J]. IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, 2001, E84D (01) : 209 - 212
  • [22] Studies on Estimation of the Number of Sources in Blind Source Separation
    Ishibashi, Takaaki
    Nakashima, Hidetoshi
    Gotanda, Hiromu
    [J]. INTERSPEECH 2008: 9TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2008, VOLS 1-5, 2008, : 163 - +
  • [23] SPARSE BLIND SOURCE SEPARATION FOR PARTIALLY CORRELATED SOURCES
    Bobin, J.
    Starck, J.
    Rapin, J.
    Larue, A.
    [J]. 2014 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 2014, : 6021 - 6025
  • [24] Compound noise separation in digital circuits using blind source separation
    Xu, Jingye
    Nigam, Vivek P.
    Roy, Abinash
    Chowdhury, Masud H.
    [J]. MICROELECTRONICS JOURNAL, 2008, 39 (08) : 1083 - 1092
  • [25] Separation of combustion noise and piston-slap in diesel engine -: Part II:: Separation of combustion noise and piston-slap using blind source separation methods
    Servière, C
    Lacoume, JL
    El Badaoui, M
    [J]. MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2005, 19 (06) : 1218 - 1229
  • [26] Noise Source Separation of an Internal Combustion Engine Based on a Single-Channel Algorithm
    Yao, Jiachi
    Xiang, Yang
    Qian, Sichong
    Wang, Shuai
    [J]. SHOCK AND VIBRATION, 2019, 2019
  • [27] Blind source separation based on subspace
    Xu, SZ
    Ye, ZF
    [J]. INTERNATIONAL SYMPOSIUM ON COMMUNICATIONS AND INFORMATION TECHNOLOGIES 2005, VOLS 1 AND 2, PROCEEDINGS, 2005, : 151 - 154
  • [28] Automatic Bayesian modal identification method for structures based on blind source separation
    Su, Liang
    Zhang, Jing-Quan
    Tang, Yu-Nan
    Huang, Xin
    [J]. AUSTRALIAN JOURNAL OF STRUCTURAL ENGINEERING, 2021, 22 (04) : 317 - 331
  • [29] A MULTICHANNEL MMSE-BASED FRAMEWORK FOR JOINT BLIND SOURCE SEPARATION AND NOISE REDUCTION
    Souden, Mehrez
    Araki, Shoko
    Kinoshita, Keisuke
    Nakatani, Tomohiro
    Sawada, Hiroshi
    [J]. 2012 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2012, : 109 - 112
  • [30] Bridge Damage Identification by Ground-based Synthetic Aperture Radar Using Blind Source Separation and Noise Reduction Technology
    Cheng, Qian-Hao
    Chen, Qiang
    Wang, Hui
    Liu, Xiang-Lei
    [J]. SENSORS AND MATERIALS, 2020, 32 (12) : 4361 - 4377