Please wait a minute...
FITEE
Front. Inform. Technol. Electron. Eng.  2018, Vol. 19 Issue (11): 1352-1361    
    
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
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
 全文: PDF 
摘要： 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    
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.
Key words: Tool     condition     monitoring         Manifold     learning         Dimensionality     reduction         Diffusion     mapping     analysis         Intrinsic
feature extraction
收稿日期: 2016-08-30 出版日期: 2019-06-13
服务　　
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章　　
Yi-xiang HUANG
Xiao LIU
Cheng-liang LIU
Yan-ming LI

引用本文:

Yi-xiang HUANG , Xiao LIU, Cheng-liang LIU , Yan-ming LI. Intrinsic feature extraction using discriminant diffusion mapping analysis for automated tool wear evaluation. Front. Inform. Technol. Electron. Eng., 2018, 19(11): 1352-1361.

链接本文:

http://www.zjujournals.com/xueshu/fitee/CN/    或    http://www.zjujournals.com/xueshu/fitee/CN/Y2018/V19/I11/1352

[1] Shafqat Ullah KHAN, M. K. A. RAHIM, Liaqat ALI. Correction of array failure using grey wolf optimizer hybridized with an interior point algorithm[J]. Front. Inform. Technol. Electron. Eng., 2018, 19(9): 1191-1202.
[2] Lan-jun LIU, Jian-fen LI , Lin ZHOU, Peng ZHAI, Hao ZHAO, Jiu-cai JIN, Zhi-chao LV. An underwater acoustic direct sequence spread spectrum communication system using dual spread spectrum code[J]. Front. Inform. Technol. Electron. Eng., 2018, 19(8): 972-983.
[3] Bo LI, Yuan-xin XU, Shuang-shuang FAN, Wen XU. Underwater docking of an under-actuated autonomous underwater vehicle: system design and control implementation[J]. Front. Inform. Technol. Electron. Eng., 2018, 19(8): 1024-1041.
[4] Lov KUMAR, Anand TIRKEY, Santanu-Ku. RATH. An effective fault prediction model developed using an extreme learning machine with various kernel methods[J]. Front. Inform. Technol. Electron. Eng., 2018, 19(7): 864-888.
[5] Yi LIN, Jian-wei ZHANG, Hong LIU. An algorithm for trajectory prediction of flight plan based on relative motion between positions[J]. Front. Inform. Technol. Electron. Eng., 2018, 19(7): 905-916.
[6] Xin CHEN, Ding WANG, Rui-rui LIU, Jie-xin YIN, Ying WU. Structural total least squares algorithm for locating multiple disjoint sources based on AOA/TOA/FOA in the presence of system error[J]. Front. Inform. Technol. Electron. Eng., 2018, 19(7): 917-936.
[7] T T DHIVYAPRABHA, P SUBASHINI, M KRISHNAVENI. Synergistic fibroblast optimization: a novel nature-inspired computing algorithm[J]. Front. Inform. Technol. Electron. Eng., 2018, 19(7): 815-833.
[8] Divya PANDOVE, Shivani GOEL, Rinkle RANI. An intuitive general rank-based correlation coefficient[J]. Front. Inform. Technol. Electron. Eng., 2018, 19(6): 699-711.
[9] Ahmad FIRDAUS, Nor Badrul ANUAR, Ahmad KARIM, Mohd Faizal Ab RAZAK. Discovering optimal features using static analysis and a genetic search based method for Android malware detection[J]. Front. Inform. Technol. Electron. Eng., 2018, 19(6): 712-736.
[10] Rabia IRFAN , Sharifullah KHAN, Kashif RAJPOOT, Ali Mustafa QAMAR. TIE algorithm: a layer over clustering-based taxonomy generation for handling evolving data[J]. Front. Inform. Technol. Electron. Eng., 2018, 19(6): 763-782.
[11] Zhen-yu LIU, Shi-en ZHOU, Jin CHENG , Chan QIU, Jian-rong TAN. Assembly variation analysis of flexible curved surfaces based on Bézier curves[J]. Front. Inform. Technol. Electron. Eng., 2018, 19(6): 796-808.
[12] Bo YU, Ying FANG, Qiang YANG, Yong TANG, Liu LIU. A survey of malware behavior description and analysis[J]. Front. Inform. Technol. Electron. Eng., 2018, 19(5): 583-603.
[13] Ke GUO, Xia-bi LIU, Lun-hao GUO , Zong-jie LI, Zeng-min GENG. A new constrained maximum margin approach to discriminative learning of Bayesian classifiers[J]. Front. Inform. Technol. Electron. Eng., 2018, 19(5): 639-650.
[14] Shang LIU, Ishtiaq AHMAD, Ping ZHANG, Zhi ZHANG. Multi-user rate and power analysis in a cognitive radio network with massive multi-input multi-output[J]. Front. Inform. Technol. Electron. Eng., 2018, 19(5): 674-684.
[15] Yi-qi XIE, Zhi-guo YU, Yang FENG, Lin-na ZHAO, Xiao-feng GU. A multistandard and resource-efficient Viterbi decoder for a multimode communication system[J]. Front. Inform. Technol. Electron. Eng., 2018, 19(4): 536-543.