Qualitative evaluation of the regeneration process of a technical object in a maintenance system with an artificial neural network

被引:5
|
作者
Duer, Stanislaw [1 ]
机构
[1] Tech Univ Koszalin, Dept Mech, PL-75620 Koszalin, Poland
来源
NEURAL COMPUTING & APPLICATIONS | 2011年 / 20卷 / 05期
关键词
Servicing process; System modelling; Expert system; Artificial neural networks; Knowledge base; Diagnostics information;
D O I
10.1007/s00521-010-0418-0
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The present article includes a description of a qualitative evaluation of the regeneration process of a reparable technical object in a maintenance system with an artificial neural network. The delineated spaces of the operational features of a new technical object and of a technical object after regeneration in a maintenance system constitute the basis of the evaluation proposed of a maintenance system. The proposed evaluation of the maintenance system is realized in a graphical and analytical method. The graphical evaluation makes the use of the spaces of maintenance information: the operation features of a new technical object (as a model) and the spaces of maintenance information after a regeneration of an object was performed in a maintenance system. For this purpose, a nominal (model) space was described and defined of maintenance information: the operational properties of a technical object and the space of theoretical maintenance information: the current operational properties of a technical object after maintenance. In the proposed qualitative analysis of regeneration, examinations covered the level of the features of the object's operation after regeneration was performed in relation to the level of the operational properties of a new object (which was only put to operation) as a model. The resulting differences in the metrics of the distances between these spaces of the object's operational properties indicate directly the errors (the qualitative evaluation level) in the organization process of the maintenance system.
引用
收藏
页码:741 / 752
页数:12
相关论文
共 50 条
  • [41] A NEURAL NETWORK TRUTH MAINTENANCE SYSTEM
    MOUNFIELD, WP
    GUDDANTI, S
    [J]. JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME, 1991, 113 (01): : 187 - 191
  • [42] Modelling of the operation process of repairable technical objects with the use information from an artificial neural network
    Duer, Stanislaw
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2011, 38 (05) : 5867 - 5878
  • [43] Using an Artificial Neural Network Approach for Supplier Evaluation Process and a Sectoral Application
    Yayla, A. Yesim
    Hartomacioglu, Selim
    [J]. PAMUKKALE UNIVERSITY JOURNAL OF ENGINEERING SCIENCES-PAMUKKALE UNIVERSITESI MUHENDISLIK BILIMLERI DERGISI, 2011, 17 (02): : 97 - 107
  • [44] Application of Artificial Neural Network in Fluid Mechanics Teaching Evaluation System
    Zhu Changjun
    Zhou Jihong
    [J]. PROCEEDINGS OF 2008 INTERNATIONAL SEMINAR ON EDUCATION MANAGEMENT AND ENGINEERING, 2008, : 505 - 508
  • [45] Application of Artificial Neural network in Fluid Mechanics Teaching Evaluation System
    Zhu, Changjun
    Hao, Zhenchun
    [J]. PROCEEDINGS OF THE FIRST INTERNATIONAL WORKSHOP ON EDUCATION TECHNOLOGY AND COMPUTER SCIENCE, VOL II, 2009, : 12 - +
  • [46] A Hybrid Artificial Neural Network for Voltage Security Evaluation in a Power System
    Zhukov, Aleksey
    Tomin, Nikita
    Sidorov, Denis
    Panasetsky, Daniil
    Spirayev, Vadim
    [J]. 2015 5TH INTERNATIONAL YOUTH CONFERENCE ON ENERGY (IYCE), 2015,
  • [47] The application of Artificial Neural Network in economic performance evaluation of Information System
    Yuan JianLin
    [J]. PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON RISK MANAGEMENT & ENGINEERING MANAGEMENT, VOLS 1 AND 2, 2008, : 839 - 843
  • [48] Performance Evaluation of an Artificial Neural Network Automatic Spindle Detection System
    Ventouras, Errikos M.
    Economou, Nicholas-Tiberio
    Kritikou, Ilia
    Tsekou, Hara
    Paparrigopoulos, Thomas J.
    Ktonas, Periklis Y.
    [J]. 2012 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2012, : 4328 - 4331
  • [49] Performance evaluation of gasification system efficiency using artificial neural network
    Ozonoh, M.
    Oboirien, B. O.
    Higginson, A.
    Daramola, M. O.
    [J]. RENEWABLE ENERGY, 2020, 145 : 2253 - 2270
  • [50] Linear transformation of qualitative criterion and artificial neural network
    Feng, Jia-Li
    Xu, Guang-Lin
    Dong, Zhan-Qiu
    Wang, Hong
    [J]. Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University, 2006, 27 (SUPPL.): : 6 - 12