Sparse representation and its applications in micro-milling condition monitoring: Noise separation and tool condition monitoring

Kunpeng Zhu, Birgit Vogel-Heuser

Research output: Contribution to journalArticlepeer-review

49 Scopus citations

Abstract

This paper presents a new approach for cutting force denoising in micro-milling condition monitoring. In micro-milling, the comparatively small cutting force signal is contaminated by heavy noise, and as a result, it is necessary to denoise the force signal before further processing it. The traditional denoising methods, based on Gaussian noise assumption, are not effective in this situation because the noise is found to contain high non-Gaussian component. Based on the force and noise's sparse structures in the time-frequency domain, this approach employs a sparse decomposition approach and solves denoising as a convex optimization problem. It is shown that the proposed approach can separate the heavy non-Gaussian noise and recover useful information for condition monitoring.

Original languageEnglish
Pages (from-to)185-199
Number of pages15
JournalInternational Journal of Advanced Manufacturing Technology
Volume70
Issue number1-4
DOIs
StatePublished - Jan 2014
Externally publishedYes

Keywords

  • Conditionmonitoring
  • Micro-milling
  • Non-Gaussian noise
  • Sparse representation

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