Journal of Computational Design and Engineering

  • 제2권2호
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  • Pages.119-127
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  • 2015
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  • 2288-4300(pISSN)
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  • 2288-5048(eISSN)
한국CDE학회 (Society for Computational Design and Engineering)
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On condition based maintenance policy

  • Shin, Jong-Ho (Department of Design and Human Engineering, UNIST) ;
  • Jun, Hong-Bae (Department of Industrial Engineering, Hongik University)
  • 투고 : 2014.11.25
  • 심사 : 2014.12.12
  • 발행 : 2015.04.01
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⟨ 이전 논문 다음 논문 ⟩

초록

In the case of a high-valuable asset, the Operation and Maintenance (O&M) phase requires heavy charges and more efforts than the installation (construction) phase, because it has long usage life and any accident of an asset during this period causes catastrophic damage to an industry. Recently, with the advent of emerging Information Communication Technologies (ICTs), we can get the visibility of asset status information during its usage period. It gives us new challenging issues for improving the efficiency of asset operations. One issue is to implement the Condition-Based Maintenance (CBM) approach that makes a diagnosis of the asset status based on wire or wireless monitored data, predicts the assets abnormality, and executes suitable maintenance actions such as repair and replacement before serious problems happen. In this study, we have addressed several aspects of CBM approach: definition, related international standards, procedure, and techniques with the introduction of some relevant case studies that we have carried out.

키워드

피인용 문헌

  1. Condition-based maintenance effectiveness for series-parallel power generation system-A combined Markovian simulation model vol.142, pp.None, 2015, https://doi.org/10.1016/j.ress.2015.04.009
  2. Cybrid 가상플랜트 시스템 요구사항 분석과 개념적 설계 vol.20, pp.4, 2015, https://doi.org/10.7315/cadcam.2015.401
  3. 플랜트 설계 및 운영 데이터 통합관리 시스템 설계 vol.42, pp.3, 2016, https://doi.org/10.7232/jkiie.2016.42.3.241
  4. Effects of condition-based maintenance on costs caused by unscheduled maintenance of aircraft vol.22, pp.4, 2015, https://doi.org/10.1108/jqme-12-2015-0062
  5. A novel integrated condition-based maintenance and stochastic flexible job shop scheduling problem: simulation-based optimization approach vol.269, pp.1, 2018, https://doi.org/10.1007/s10479-017-2594-0
  6. The Predictive Maintenance Concept in the Maintenance Department of the “Industry 4.0” Production Enterprise vol.10, pp.1, 2015, https://doi.org/10.2478/fman-2018-0022
  7. Monitoreo de la degradación de los vehículos de transporte de cargas a través de la disponibilidad vol.85, pp.205, 2015, https://doi.org/10.15446/dyna.v85n205.68443
  8. Preliminary study of the vibration-based maintenance implementation: case study vol.24, pp.2, 2015, https://doi.org/10.1108/jqme-10-2016-0047
  9. Condition-based Maintenance (CBM) 적용 장비 선정을 위한 기본설계 단계의 LNG-FPSO 가용도 추산 vol.42, pp.6, 2018, https://doi.org/10.5916/jkosme.2018.42.6.394
  10. Identification of maintenance factors influencing the development of sustainable production processes - a pilot study vol.400, pp.None, 2015, https://doi.org/10.1088/1757-899x/400/6/062014
  11. A procedure for condition-based maintenance and diagnostics of submersible well pumps through vibration monitoring vol.9, pp.5, 2015, https://doi.org/10.1007/s13198-018-0711-3
  12. The Maintenance Scheduling Issues of Railway Traffic Control Systems with Respect to Reliability vol.47, pp.1, 2015, https://doi.org/10.2478/jok-2018-0044
  13. A Review in Fault Diagnosis and Health Assessment for Railway Traction Drives vol.8, pp.12, 2018, https://doi.org/10.3390/app8122475
  14. Customized Knowledge Discovery in Databases methodology for the Control of Assembly Systems vol.6, pp.4, 2018, https://doi.org/10.3390/machines6040045
  15. Condition-based maintenance strategy for vehicles using hidden Markov models vol.11, pp.1, 2015, https://doi.org/10.1177/1687814018806380
  16. Service-Oriented Computing for intelligent train maintenance vol.13, pp.1, 2015, https://doi.org/10.1080/17517575.2018.1501818
  17. Joint Optimization of Production and Maintenance Using Monte Carlo Method and Metaheuristic Algorithms vol.2019, pp.None, 2015, https://doi.org/10.1155/2019/3670495
  18. Running Smart Monitoring Maintenance Application Using Cooja Simulator vol.42, pp.None, 2015, https://doi.org/10.4028/www.scientific.net/jera.42.149
  19. An Approach to Supporting the Selection of Maintenance Experts in the Context of Industry 4.0 vol.9, pp.9, 2015, https://doi.org/10.3390/app9091848
  20. Joint optimal production planning and proactive maintenance policy for a system subject to degradation vol.25, pp.2, 2019, https://doi.org/10.1108/jqme-11-2016-0068
  21. General overview of maintenance strategies - concepts and approaches vol.2, pp.1, 2015, https://doi.org/10.2478/mape-2019-0013
  22. Multi-objective Bayesian optimization of super hydrophobic coatings on asphalt concrete surfaces vol.6, pp.4, 2019, https://doi.org/10.1016/j.jcde.2018.11.005
  23. Health stages diagnostics of underwater thruster using sound features with imbalanced dataset vol.31, pp.10, 2015, https://doi.org/10.1007/s00521-018-3407-3
  24. Integrated Scheduling of Production and Maintenance for Continuous Plants: Conditional Sequencing and Approximate Modeling of Storage vol.58, pp.41, 2015, https://doi.org/10.1021/acs.iecr.9b01984
  25. A Review on Intelligent Fault Detection in Rolling Element Bearings vol.184, pp.None, 2015, https://doi.org/10.1051/e3sconf/202018401044
  26. Determining the optimal inspection rate of circuit breakers equipped with condition monitoring devices using new maintenance Markov model vol.30, pp.4, 2015, https://doi.org/10.1002/2050-7038.12272
  27. Condition-Based Maintenance-An Extensive Literature Review vol.8, pp.2, 2015, https://doi.org/10.3390/machines8020031
  28. Application of condition‐based maintenance in health care: A concept analysis vol.55, pp.3, 2015, https://doi.org/10.1111/nuf.12456
  29. Safety barriers: Research advances and new thoughts on theory, engineering and management vol.67, pp.None, 2015, https://doi.org/10.1016/j.jlp.2020.104260
  30. Roller Leveler Monitoring Using Acceleration Measurements and Models for Steel Strip Properties vol.8, pp.3, 2015, https://doi.org/10.3390/machines8030043
  31. Predictive Maintenance Using Machine Learning and Data Mining: A Pioneer Method Implemented to Greek Railways vol.5, pp.1, 2015, https://doi.org/10.3390/designs5010005
  32. Predictive modelling of turbofan engine components condition using machine and deep learning methods vol.23, pp.2, 2015, https://doi.org/10.17531/ein.2021.2.16
  33. A Novel Condition Monitoring Procedure for Early Detection of Copper Corrosion Problems in Oil-Filled Electrical Transformers vol.14, pp.14, 2015, https://doi.org/10.3390/en14144266
  34. Nuclear Operating Experience Review and User Needs Assessment for a Risk-informed Predictive Maintenance Human System Interface vol.65, pp.1, 2015, https://doi.org/10.1177/1071181321651272
  35. Big Machinery Data Preprocessing Methodology for Data-Driven Models in Prognostics and Health Management vol.21, pp.20, 2015, https://doi.org/10.3390/s21206841
  36. Methodology for the integration of a high-speed train in Maintenance 4.0 vol.8, pp.6, 2015, https://doi.org/10.1093/jcde/qwab064
  37. A Study on PF-IFF-Based Diagnosis Model of Plant Equipment Failure vol.12, pp.1, 2015, https://doi.org/10.3390/app12010347