ROBUST INTERVAL-BASED MINIMAX-REGRET ANALYSIS METHOD FOR FILTER MANAGEMENT OF FLUID POWER SYSTEM

Songlin Nie (), Hui Ji (), Yeqing Huang (), Zhen Hu () and Yongping Li ()
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Songlin Nie: College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing 100124, P. R. China
Hui Ji: College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing 100124, P. R. China
Yeqing Huang: College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing 100124, P. R. China
Zhen Hu: Department of Mechanical and Aerospace Engineering, Missouri University of Science and Technology, Rolla, Missouri 65401, USA
Yongping Li: S-C Energy and Environmental Research Academy, North China Electric Power University, Beijing 102206, P. R. China

Asia-Pacific Journal of Operational Research (APJOR), 2013, vol. 30, issue 06, 1-40

Abstract: Fluid contamination is one of the main reasons for the wear failure and the related downtime in a hydraulic power system. Filters play an important role in controlling the contamination effectively, increasing the reliability of the system, and maintaining the system economically. Due to the uncertainties of system parameters, the complicated relationship among components, as well as the lack of effective approach, managing filters is becoming one of the biggest challenges for engineers and decision makers. In this study, a robust interval-based minimax-regret analysis (RIMA) method is developed for the filter management in a fluid power system (FPS) under uncertainty. The RIMA method can handle the uncertainties existed in contaminant ingressions of the system and contaminant holding capacity of filters without making assumption on probabilistic distributions for random variables. Through analyzing the system cost of all possible filter management alternatives, an interval element regret matrix can be obtained, which enables decision makers to identify the optimal filter management strategy under uncertainty. The results of a case study indicate that the reasonable solutions generated can help decision makers understand the consequence of short-term and long-term decisions, identify optimal strategies for filter allocation and selection with minimized system-maintenance cost and system-failure risk.

Keywords: Contamination control; decision making; fluid power system; minimax-regret; robust programming; uncertainty (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1142/S0217595913500218

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