|
|
Intrinsic feature extraction using discriminant diffusion mapping analysis for automated tool wear evaluation |
Yi-xiang HUANG , Xiao LIU, Cheng-liang LIU , Yan-ming LI |
State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, China
Shanghai Aerospace Equipment Manufacturer, Shanghai 200245, China |
|
|
Abstract We present a method of discriminant diffusion maps analysis (DDMA) for evaluating tool wear during milling pro-
cesses. 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.
|
Received: 30 August 2016
Published: 13 June 2019
|
Intrinsic feature extraction using discriminant diffusion mapping analysis for automated tool wear evaluation
We present a method of discriminant diffusion maps analysis (DDMA) for evaluating tool wear during milling pro-
cesses. 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.
关键词:
Tool ,
condition ,
monitoring,
 ,
Manifold ,
learning,
 ,
Dimensionality ,
reduction,
 ,
Diffusion ,
mapping ,
analysis,
 ,
Intrinsic
feature extraction
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
|
Shared |
|
|
|
|
|
Discussed |
|
|
|
|