CLC number: TP277
On-line Access: 2018-12-14
Received: 2016-08-30
Revision Accepted: 2017-01-23
Crosschecked: 2018-11-27
Cited: 0
Clicked: 6871
Yi-xiang Huang, Xiao Liu, Cheng-liang Liu, Yan-ming Li. Intrinsic feature extraction using discriminant diffusion mapping analysis for automated tool wear evaluation[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(11): 1352-1361.
@article{title="Intrinsic feature extraction using discriminant diffusion mapping analysis for automated tool wear evaluation",
author="Yi-xiang Huang, Xiao Liu, Cheng-liang Liu, Yan-ming Li",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="19",
number="11",
pages="1352-1361",
year="2018",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1601512"
}
%0 Journal Article
%T Intrinsic feature extraction using discriminant diffusion mapping analysis for automated tool wear evaluation
%A Yi-xiang Huang
%A Xiao Liu
%A Cheng-liang Liu
%A Yan-ming Li
%J Frontiers of Information Technology & Electronic Engineering
%V 19
%N 11
%P 1352-1361
%@ 2095-9184
%D 2018
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1601512
TY - JOUR
T1 - Intrinsic feature extraction using discriminant diffusion mapping analysis for automated tool wear evaluation
A1 - Yi-xiang Huang
A1 - Xiao Liu
A1 - Cheng-liang Liu
A1 - Yan-ming Li
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 19
IS - 11
SP - 1352
EP - 1361
%@ 2095-9184
Y1 - 2018
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1601512
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.
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