VALUE OF INFORMATION FOR CONTINUOUS MONITORING SYSTEMS IN RECURRENT MAINTENANCE DECISION SCENARIOS

被引:0
作者
Liu, Xinyang [1 ]
Wang, Pingfeng [1 ]
机构
[1] Univ Illinois, Ind & Enterprise Syst Engn, Urbana, IL 61801 USA
来源
PROCEEDINGS OF ASME 2021 INTERNATIONAL DESIGN ENGINEERING TECHNICAL CONFERENCES AND COMPUTERS AND INFORMATION IN ENGINEERING CONFERENCE, IDETC-CIE2021, VOL 3A | 2021年
基金
美国国家科学基金会;
关键词
Value of information; monitoring systems; failure prediction; maintenance; design decision making; DESIGN OPTIMIZATION; ROBUST DESIGN; NETWORKS;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Monitoring systems play a crucial role in improving system failure resilience and preventing tragic consequences brought by unexpected system failure and saving the consequential high cost. Continuous monitoring systems have been applied to diversified systems for well-informed operations. Although plenty research has devoted to predicting system states using the continuous data flow, there still lacks a systematic decision-making framework for system designers and engineering system owners to maximize their benefits on adopting monitoring systems. This paper constructs such a decision-making framework, with which system owners can evaluate the operation cost change under specific operation modes considering the effectiveness of continuous monitoring systems in predicting system failures. Two case studies have been conducted to illustrate the value evaluation of the monitoring information and the system maintenance process with the aid of different prognostic results based on the monitoring data. The first case study considers a health-state prediction with fixed accuracy while the second one incorporates the accuracy improvement as the monitoring data accumulates. Results show that the value of monitoring systems will be influenced by the deviation among the equipment group, the accuracy of system-state prediction, and different types of cost involved in the operating process. And the adjustment of maintenance actions based on monitoring and prognosis information will help improve the value of monitoring systems.
引用
收藏
页数:9
相关论文
共 31 条
  • [1] A review on condition-based maintenance optimization models for stochastically deteriorating system
    Alaswad, Suzan
    Xiang, Yisha
    [J]. RELIABILITY ENGINEERING & SYSTEM SAFETY, 2017, 157 : 54 - 63
  • [2] Cost efficient robust global supply chain system design under uncertainty
    Almaktoom, Abdulaziz T.
    Krishnan, Krishna K.
    Wang, Pingfeng
    Alsobhi, Samir
    [J]. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2016, 85 (1-4) : 853 - 868
  • [3] Prognostics Using an Adaptive Self-Cognizant Dynamic System Approach
    Bai, Guangxing
    Wang, Pingfeng
    [J]. IEEE TRANSACTIONS ON RELIABILITY, 2016, 65 (03) : 1427 - 1437
  • [4] Disassembly and Reassembly Sequence Planning Tradeoffs Under Uncertainty for Product Maintenance
    Behdad, Sara
    Thurston, Deborah
    [J]. JOURNAL OF MECHANICAL DESIGN, 2012, 134 (04)
  • [5] Stochastic modeling and real-time prognostics for multi-component systems with degradation rate interactions
    Bian, Linkan
    Gebraeel, Nagi
    [J]. IIE TRANSACTIONS, 2014, 46 (05) : 470 - 482
  • [6] A Modular Design Approach to Improve Product Life Cycle Performance Based on the Optimization of a Closed-Loop Supply Chain
    Chung, Wu-Hsun
    Kremer, Guel E. Okudan
    Wysk, Richard A.
    [J]. JOURNAL OF MECHANICAL DESIGN, 2014, 136 (02)
  • [7] Adaptive design optimization of wireless sensor networks using genetic algorithms
    Ferentinos, Konstantinos P.
    Tsiligiridis, Theodore A.
    [J]. COMPUTER NETWORKS, 2007, 51 (04) : 1031 - 1051
  • [8] Gorjian N., 2010, ENG ASSET LIFECYCLE, P385, DOI DOI 10.1007/978-0-85729-320-6_43
  • [9] Gorjian N., 2010, Engineering Asset Lifecycle Management, P369, DOI [10.1007/978-0-85729-320-6-42, DOI 10.1007/978-0-85729-320-6-42]
  • [10] A maintenance optimization model for mission-oriented systems based on Wiener degradation
    Guo, Chiming
    Wang, Wenbin
    Guo, Bo
    Si, Xiaosheng
    [J]. RELIABILITY ENGINEERING & SYSTEM SAFETY, 2013, 111 : 183 - 194