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A novel sound-based belt condition monitoring method for robotic grinding using optimally pruned extreme learning machine

  • Published:2018-10 Issue: Volume:260 Page:9-19
  • ISSN:0924-0136
  • Container-title:Journal of Materials Processing Technology
  • language:en
  • Short-container-title:Journal of Materials Processing Technology

Author:

Zhang Xiaoqiang,Chen Huabin,Xu Jijin,Song Xuefeng,Wang Junwei,Chen Xiaoqi

Funder

Shanghai Jiaotong University Start-up Grant of China 1000Plan

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Metals and Alloys,Computer Science Applications,Modelling and Simulation,Ceramics and Composites

Reference24 articles.

1. Assessment of grinding wheel conditioning process using machine vision;Arunachalam;Progn. Health Manage.,2015

2. Cluster characteristics of Geldart Group B particles in a pilot-scale CFB riser. I. Monodisperse systems;Chew;Chem. Eng. Sci.,2012

3. Fault detection in induction machines using power spectral density in wavelet decomposition;CusidÓCusido;IEEE Trans. Ind. Electron.,2008

4. A New model for grinding force prediction and analysis;Durgumahanti;Int. J. Mach. Tools Manuf.,2010

5. Acoustic assessment of grinding-wheel life in the bilateral face grinding of thick-walled blanks;Evstigneev;Russ. Eng. Res.,2013

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