A Summary of Health Prognostics Methods for Industrial Robots

被引：11
|
作者：
Zhou, Qiaoqian [1 ]
Wang, Yuanchao [1 ]
Xu, Jianming [1 ]
机构：
[1] Zhejiang Univ Technol, Coll Informat Engn, Hangzhou, Peoples R China
来源：
2019 PROGNOSTICS AND SYSTEM HEALTH MANAGEMENT CONFERENCE (PHM-QINGDAO) | 2019年
关键词：
robot; remaining useful life; prognostics and health management; industrial robot; USEFUL LIFE PREDICTION; FAULT-DIAGNOSIS; SYSTEM; PROPAGATION; DESIGN; MOTORS; CLOUD; MODEL; RUL;
D O I：
10.1109/phm-qingdao46334.2019.8942969
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
With the rise of intelligent manufacturing, the requirements for precision and reliability of industrial robots are increasing. Through the industrial robot system status monitoring and health prognostics, when maintenance is needed, this strategy of condition-based maintenance (CBM) can reduce unnecessary maintenance operations effectively, and reduce the overall maintenance cost. The health prognostics usually consists of data acquisition and processing, health indicator (HI) construction and remaining useful life (RUL) prediction. Industrial robots are complex systems composed of sensors, reducers, motors, servo drivers and controllers. In this paper, the methods of health prognostics are summarized from two aspects: component level and system level. Finally, the health prognostics methods for industrial robots are prospected and summarized combined with literature.
引用
收藏
页数：6
相关论文
共 50 条
  • [31] Optimal Prognostics and Health Management-driven inspection and maintenance strategies for industrial systems
    Mancuso, A.
    Compare, M.
    Salo, A.
    Zio, E.
    [J]. RELIABILITY ENGINEERING & SYSTEM SAFETY, 2021, 210
  • [32] A study on data augmentation optimization for data-centric health prognostics of industrial systems
    Gay, Antonin
    Voisin, Alexandre
    Iung, Benoit
    Do, Phuc
    Bonidal, Remi
    Khelassi, Ahmed
    [J]. IFAC PAPERSONLINE, 2023, 56 (02): : 1270 - 1275
  • [33] Prognostics Methods of Security Phenomena
    Smolik, Josef
    [J]. VOJENSKE ROZHLEDY-CZECH MILITARY REVIEW, 2018, 27 (01): : 3 - 23
  • [34] A review of artificial intelligence methods for engineering prognostics and health management with implementation guidelines
    Khanh T. P. Nguyen
    Kamal Medjaher
    Do T. Tran
    [J]. Artificial Intelligence Review, 2023, 56 : 3659 - 3709
  • [35] Diagnostic Ability Validation Methods for Prognostics and Health Management Systems in Complex Systems
    Du, Yi
    Song, Chengjun
    Su, Feng
    Wang, Zhili
    [J]. PROCEEDINGS OF 2016 11TH INTERNATIONAL CONFERENCE ON RELIABILITY, MAINTAINABILITY AND SAFETY (ICRMS'2016): INTEGRATING BIG DATA, IMPROVING RELIABILITY & SERVING PERSONALIZATION, 2016,
  • [36] A review on prognostics and health management (PHM) methods of lithium-ion batteries
    Meng, Huixing
    Li, Yan-Fu
    [J]. RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2019, 116
  • [37] Standardizing Research Methods for Prognostics
    Uckun, Serdar
    Goebel, Kai
    Lucas, Peter J. F.
    [J]. 2008 INTERNATIONAL CONFERENCE ON PROGNOSTICS AND HEALTH MANAGEMENT (PHM), 2008, : 191 - +
  • [38] Prognostics implementation methods for electronics
    Gu, Jie
    Vichare, Nikhil
    Tracy, Terry
    Pecht, Michael
    [J]. ANNUAL RELIABILITY AND MAINTAINABILITY SYMPOSIUM, 2007 PROCEEDINGS, 2006, : 101 - +
  • [39] A review of artificial intelligence methods for engineering prognostics and health management with implementation guidelines
    Nguyen, Khanh T. P.
    Medjaher, Kamal
    Tran, Do T.
    [J]. ARTIFICIAL INTELLIGENCE REVIEW, 2023, 56 (04) : 3659 - 3709
  • [40] The Aramis Data Challenge to prognostics and health management methods for application in evolving environments
    Baraldi, Piero
    Compare, Michele
    Zio, Enrico
    Cannarile, Francesco
    Yang, Zhe
    [J]. PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART O-JOURNAL OF RISK AND RELIABILITY, 2023, 237 (05) : 958 - 965
12345