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

Yi-xiang HUANG; Xiao LIU; Cheng-liang LIU; Yan-ming LI

doi:10.1631/FITEE.1601512

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

Regular Papers | Updated：2022-05-19

- Intrinsic feature extraction using discriminant diffusion mapping analysis for automated tool wear evaluation
- 基于判别扩散映射分析的内蕴特征提取方法在刀具磨损评估中的应用
- Frontiers of Information Technology & Electronic Engineering Vol. 19, Issue 11, Pages: 1352-1361(2018)
- Affiliations：
  
  State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, China
  Shanghai Aerospace Equipment Manufacturer, Shanghai 200245, China
- Author bio：
  
  [ "Yi-xiang HUANG, E-mail: huang.yixiang@sjtu.edu.cn" ]
- Funds：
- DOI：10.1631/FITEE.1601512
  CLC： TP277
- Received：30 August 2016，
  
  Revised：2018-11-27，
  
  Published：2018-11
- Accepted：
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Yi-xiang HUANG, Xiao LIU, Cheng-liang LIU, et al. 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.
DOI：

Yi-xiang HUANG, Xiao LIU, Cheng-liang LIU, et al. 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. DOI： 10.1631/FITEE.1601512.

摘要

针对铣削加工刀具磨损评估，提出一种基于判别扩散映射分析的方法。判别扩散映射分析（discriminant diffusion maps analysis，DDMA）用于时频域特征提取融合与维度缩减。通过保持内蕴特征空间的扩散距离，耦合时频域特征和判别内核，提取有效信息。该方法包含3个步骤：(1)信号处理与特征提取；(2)内蕴维度估计；(3)基于扩散距离保持的特征融合实现。将该方法应用于数控加工中心主轴驱动电流信号，评估刀具磨损状态。与常见主成分分析方法相比，该方法能更好保持刀具磨损状态相关的有用内蕴信息，大大降低与刀具磨损相关的特征维度，并可直接应用于大多数工业数控加工中心，方便获取电流信号实际情况。

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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Keywords

references

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