Tool condition monitoring based on fractal and wavelet analysis by acoustic emission

被引：0
|
作者：
Song, Wanqing [1 ]
Yang, Jianguo [1 ]
Qiang, Chen [2 ]
机构：
[1] Donghua Univ, Coll Mech Engn, 2999 Renmin N Rd,Songjiang Dist, Shanghai 201620, Peoples R China
[2] Shanghai Univ Engn & Sci, Comp Ctr, Shanghai 201620, Peoples R China
来源：
COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2007, PT 1, PROCEEDINGS | 2007年 / 4705卷
基金：
上海市自然科学基金;
关键词：
fractal dimension; wavelets; correlation dimension; acoustic emission; tool condition monitoring;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this article, a technique based on the acoustic emission (AE) signal fractal and wavelet analysis are proposed for tool condition monitoring. it is difficult to obtain an effective result by these raw acoustic emission data. The local characterize of frequency band, which contains the main energy of AE signals, is depicted by the wavelet multi-resolution analysis, fractal dimension can describe the complexity of time series. It is found that the fault signal can effectively be extracted by wavelet transform and fractal dimension. Experimental results prove that this method is effectively.
引用
收藏
页码：469 / +
页数：3
相关论文
共 50 条
  • [41] Tool condition monitoring based on the fractal analysis of current and cutting force signals during CFRP trimming
    Maryam Jamshidi
    Jean-François Chatelain
    Xavier Rimpault
    Marek Balazinski
    [J]. The International Journal of Advanced Manufacturing Technology, 2022, 121 : 8127 - 8142
  • [42] Tool condition monitoring based on the fractal analysis of current and cutting force signals during CFRP trimming
    Jamshidi, Maryam
    Chatelain, Jean-Francois
    Rimpault, Xavier
    Balazinski, Marek
    [J]. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2022, 121 (11-12): : 8127 - 8142
  • [43] Condition Monitoring of Milling Tool Wear Based on Fractal Dimension of Vibration Signals
    Xu, Chuangwen
    Chen, Hualing
    Liu, Zhe
    Cheng, Zhongwen
    [J]. STROJNISKI VESTNIK-JOURNAL OF MECHANICAL ENGINEERING, 2009, 55 (01): : 15 - 25
  • [44] Diamond Grinding Wheel Condition Monitoring Based on Acoustic Emission Signals
    Bi, Guo
    Liu, Shan
    Su, Shibo
    Wang, Zhongxue
    [J]. SENSORS, 2021, 21 (04) : 1 - 17
  • [45] Lubrication Condition Monitoring and Evaluation of Rolling Bearing Based on Acoustic Emission
    Duan, Jinjin
    Xia, Zhou
    Xu, GuangHua
    Dan, Ziyan
    Zhang, SiCong
    Liang, Lin
    [J]. 2018 IEEE INTERNATIONAL CONFERENCE ON PROGNOSTICS AND HEALTH MANAGEMENT (ICPHM), 2018,
  • [46] Condition Monitoring of Shaft Crack with Acoustic Emission
    Lin, Li
    Zhou, Yong
    Chu, Fulei
    Lu, Wenxiu
    [J]. ADVANCES IN ACOUSTIC EMISSION TECHNOLOGY, 2015, 158 : 567 - 574
  • [47] Acoustic emission sources in bearing condition monitoring
    Lees, A. W.
    Quiney, Z.
    Ganji, A.
    Murray, B.
    [J]. 8TH INTERNATIONAL CONFERENCE ON CONDITION MONITORING AND MACHINERY FAILURE PREVENTION TECHNOLOGIES 2011, VOLS 1 AND 2, 2011, : 522 - 532
  • [48] ACOUSTIC EMISSION CONDITION MONITORING OF THE CORONING PROCESS
    Yum, Juil
    Kamouneh, Amir
    Wang, Wencai
    Kannatey-Asibu, Elijah, Jr.
    [J]. PROCEEDINGS OF THE ASME INTERNATIONAL MANUFACTURING SCIENCE AND ENGINEERING CONFERENCE, VOL 2, 2009, : 157 - 163
  • [49] THE USE OF ACOUSTIC EMISSION FOR BEARING CONDITION MONITORING
    Lees, A. W.
    Quiney, Z.
    Ganji, A.
    Murray, B.
    [J]. 9TH INTERNATIONAL CONFERENCE ON DAMAGE ASSESSMENT OF STRUCTURES (DAMAS 2011), 2011, 305
  • [50] Wavelet Based Sensor Fusion for Tool Condition Monitoring of Hard to Machine Materials
    Niaki, Farbod Akhavan
    Ulutan, Durul
    Mears, Laine
    [J]. 2015 IEEE INTERNATIONAL CONFERENCE ON MULTISENSOR FUSION AND INTEGRATION FOR INTELLIGENT SYSTEMS (MFI), 2015, : 271 - 276
12345