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Intrinsic feature extraction using discriminant diffusion mapping analysis for automated tool wear evaluation

  • Yi-xiang HuangEmail author
  • Xiao Liu
  • Cheng-liang Liu
  • Yan-ming Li
Article
  • 19 Downloads

Abstract

We present a method of discriminant diffusion maps analysis (DDMA) for evaluating tool wear during milling processes. As a dimensionality reduction technique, the DDMA method is used to fuse and reduce the original features extracted from both the time and frequency domains, by preserving the diffusion distances within the intrinsic feature space and coupling the features to a discriminant kernel to refine the information from the high-dimensional feature space. The proposed DDMA method consists of three main steps: (1) signal processing and feature extraction; (2) intrinsic dimensionality estimation; (3) feature fusion implementation through feature space mapping with diffusion distance preservation. DDMA has been applied to current signals measured from the spindle in a machine center during a milling experiment to evaluate the tool wear status. Compared with the popular principle component analysis method, DDMA can better preserve the useful intrinsic information related to tool wear status. Thus, two important aspects are highlighted in this study: the benefits of the significantly lower dimension of the intrinsic features that are sensitive to tool wear, and the convenient availability of current signals in most industrial machine centers.

Key words

Tool condition monitoring Manifold learning Dimensionality reduction Diffusion mapping analysis Intrinsic feature extraction 

CLC number

TP277 

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Copyright information

© Editorial Office of Journal of Zhejiang University Science and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.State Key Laboratory of Mechanical System and VibrationShanghai Jiao Tong UniversityShanghaiChina
  2. 2.Shanghai Aerospace Equipment ManufacturerShanghaiChina

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