Fault decision of computer numerical control machine system using grey clustering analysis and rough set theory

被引:6
|
作者
Ren, Xianlin [1 ,2 ]
Chen, Leo [3 ]
Li, DeShun [1 ]
Pang, ZeZhao [1 ]
机构
[1] Univ Elect Sci & Technol China, Sch Mechatron Engn, Chengdu 611731, Sichuan, Peoples R China
[2] UESTC Guangdong, Inst Elect & Informat Engn, Dongguan, Peoples R China
[3] Swansea Univ, Coll Engn, Bay Campus, Swansea SA1 8EN, W Glam, Wales
基金
中国国家自然科学基金;
关键词
Motion micro-unit; fault classification; grey clustering decision; rough set; knowledge reduction; FAILURE MODE; OPTIMIZATION; ALGORITHM; DESIGN; FUZZY;
D O I
10.1177/1687814019852846
中图分类号
O414.1 [热力学];
学科分类号
摘要
The computer numerical control machine is important industrial equipment, and its reliability has been one of the most important symbols to measure the modernization of advanced manufacturing and it is critical in the aspects of reliability design improvement, fault monitoring, and fault repair for the computer numerical control machine. The computer numerical control machine's assembly process is a significant part in its manufacturing process, and assembly operation is a major factor in determining the whole machine's quality, and assembly process quality analysis is necessary for computer numerical control machines, in which reliability allocation is an essential part of its reliability design. In order to quickly locate the fault of computer numerical control machine tool and accurately judge the fault grade, a method of fault classification decision of computer numerical control machine tool based on motion micro-unit is proposed, which includes the following steps: (1) from the point of the decomposition of system function, the computer numerical control machine tool is decomposed layer by layer into the layer of micro-actions, and the conceptual model of motion unit is given; (2) from the level of action, the types of fault modes of motion units are comprehensively analyzed and summarized; and (3) combining grey clustering theory and rough set theory, a fast and accurate fault classification decision-making method is formed. Finally, the validity of this method is verified by an example analysis of motion micro-units of a computer numerical control machine rack. The contributions of this work can be summarized as (1) the proposed grey fixed weight clustering analysis, (2) the graded fault classification using the decision table approach, (3) the knowledge reduction of decision rules using the rough set theory, and (4) the quicker and accurater decision and effectiveness validated by the given study case.
引用
收藏
页数:11
相关论文
共 50 条
  • [1] HVAC Fault Diagnosis System Using Rough Set Theory and Support Vector Machine
    Li Xuemei
    Shao Ming
    Ding Lixing
    [J]. WKDD: 2009 SECOND INTERNATIONAL WORKSHOP ON KNOWLEDGE DISCOVERY AND DATA MINING, PROCEEDINGS, 2009, : 895 - +
  • [2] A clinical decision support system using rough set theory and machine learning for disease prediction
    Singh, Kamakhya Narain
    Mantri, Jibendu Kumar
    [J]. INTELLIGENT MEDICINE, 2024, 4 (03): : 200 - 208
  • [3] Fragile Watermarking of Decision System Using Rough Set Theory
    Khanduja, Vidhi
    Chakraverty, Shampa
    [J]. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING, 2018, 43 (12) : 7621 - 7633
  • [4] Fault diagnosis system based on rough set theory and support vector machine
    Xu, YT
    Wang, LS
    [J]. FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, PT 2, PROCEEDINGS, 2005, 3614 : 980 - 988
  • [5] Fragile Watermarking of Decision System Using Rough Set Theory
    Vidhi Khanduja
    Shampa Chakraverty
    [J]. Arabian Journal for Science and Engineering, 2018, 43 : 7621 - 7633
  • [6] Discretization using clustering and rough set theory
    Singh, Girish Kumar
    Minz, Sonajharia
    [J]. ICCTA 2007: INTERNATIONAL CONFERENCE ON COMPUTING: THEORY AND APPLICATIONS, PROCEEDINGS, 2007, : 330 - +
  • [7] Autonomous Clustering Using Rough Set Theory
    Bean, Charlotte
    Kambhampati, Chandra
    [J]. INTERNATIONAL JOURNAL OF AUTOMATION AND COMPUTING, 2008, 5 (01) : 90 - 102
  • [8] Autonomous Clustering Using Rough Set Theory
    Charlotte Bean
    Chandra Kambhampati
    [J]. Machine Intelligence Research, 2008, (01) : 90 - 102
  • [9] Fault prediction of fire control system based on Grey rough set and BP neural network
    Xie, Baoqi
    Li, Yingshun
    Liu, Haiyang
    Kang, Xingru
    Zhang, Yang
    [J]. 2022 PROGNOSTICS AND HEALTH MANAGEMENT CONFERENCE, PHM-LONDON 2022, 2022, : 1 - 5
  • [10] Rough set theory analysis on decision subdivision
    Xu, JC
    Shen, JY
    Wang, GY
    [J]. ROUGH SETS AND CURRENT TRENDS IN COMPUTING, 2004, 3066 : 340 - 345