Modeling changes in maintenance activities through fine-tuning Markov models of ageing equipment

被引:0
|
作者
Sugier, Jaroslaw [1 ]
Anders, George J. [2 ]
机构
[1] Wroclaw Univ Technol, Wroclaw, Poland
[2] Tech Univ Lodz, PL-90924 Lodz, Poland
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Markov models are well established technique used widely for modeling equipment deterioration. This work presents an approach where Markov models represent equipment ageing and also incorporate various maintenance activities. Having available some basic model representing both deterioration and maintenance processes it is possible to adjust its parameters so that it corresponds to some hypothetical new maintenance policy and then to examine impact that this new policy has on various reliability characteristics of the system. The paper presents a method of model adjustment and discusses implementation of three numerical algorithms solving the problem of parameter approximation. A practical example confirms validity of the approach and illustrates its efficiency.
引用
收藏
页码:336 / +
页数:2
相关论文
共 50 条
  • [1] Modifying Markov Models of Ageing Equipment for Modeling Changes in Maintenance Policies
    Sugier, Jaroslaw
    Anders, George J.
    PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DEPENDABILITY OF COMPUTER SYSTEMS, 2009, : 348 - 355
  • [2] Fine-tuning constraints on supergravity models
    Bastero-Gil, M
    Kane, GL
    King, SF
    PHYSICS LETTERS B, 2000, 474 (1-2) : 103 - 112
  • [3] Fine-tuning established morphometric models through citizen science data
    Biskis, Veronika N.
    Townsend, Kathy A.
    Morgan, David L.
    Lear, Karissa O.
    Holmes, Bonnie J.
    Wueringer, Barbara E.
    CONSERVATION SCIENCE AND PRACTICE, 2025, 7 (03)
  • [4] Verification of Markov models of ageing power equipment
    Sugier, Jaroslaw
    Anders, George J.
    2008 10TH INTERNATIONAL CONFERENCE ON PROBABILISTIC METHODS APPLIED TO POWER SYSTEMS, 2008, : 176 - +
  • [5] FINE-TUNING OF ECONOMIC-FORECASTING MODELS
    HUJER, R
    CREMER, R
    KNEPEL, H
    JAHRBUCHER FUR NATIONALOKONOMIE UND STATISTIK, 1979, 194 (01): : 41 - 70
  • [6] Improving fine-tuning in composite Higgs models
    Banerjee, Avik
    Bhattacharyya, Gautam
    Ray, Tirtha Sankar
    PHYSICAL REVIEW D, 2017, 96 (03)
  • [7] Quantum fine-tuning in stringy quintessence models
    Hertzberg, Mark R.
    Sandora, McCullen
    Trodden, Mark
    PHYSICS LETTERS B, 2019, 797
  • [8] The fine-tuning cost of the likelihood in SUSY models
    Ghilencea, D. M.
    Ross, G. G.
    NUCLEAR PHYSICS B, 2013, 868 (01) : 65 - 74
  • [9] Learning from models beyond fine-tuning
    Zheng, Hongling
    Shen, Li
    Tang, Anke
    Luo, Yong
    Hu, Han
    Du, Bo
    Wen, Yonggang
    Tao, Dacheng
    NATURE MACHINE INTELLIGENCE, 2025, 7 (01) : 6 - 17
  • [10] Efficient Fine-Tuning of BERT Models on the Edge
    Vucetic, Danilo
    Tayaranian, Mohammadreza
    Ziaeefard, Maryam
    Clark, James J.
    Meyer, Brett H.
    Gross, Warren J.
    2022 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS 22), 2022, : 1838 - 1842