Open AccessMarkov Reward Approach and Reliability Associated Cost Model for Machine Tools Maintenance-Planning Optimization
Author(s) -
Weiliang Zeng,
Ilia Frenkel,
Guixiang Shen,
Igor Bolvashenkov,
Jörg Kammermann,
Hans-Georg Herzog,
Lev Khvatskin
Publication year - 2019
Publication title -
international journal of mathematical, engineering and management sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.228
H-Index - 10
ISSN - 2455-7749
DOI - 10.33889/ijmems.2019.4.4-065
Subject(s) - reliability engineering , reliability (semiconductor) , optimal maintenance , computer science , preventive maintenance , maintenance actions , markov chain , markov model , corrective maintenance , genetic algorithm , markov process , engineering , machine learning , mathematics , power (physics) , physics , quantum mechanics , statistics
This paper proposes a novel Reliability Associated Cost (RAC) model for machine tools throughout its lifetime that considers two different failure consequences, immediate failure and product rejections increase failure. A maintenance strategy of corrective maintenance combined with overhaul utilized to the maintenance activities of machine tools in the current paper. Markov reward approach is developed for computing of the costs incurred by both failure consequences and maintenance activities and system average availability throughout the machine tools life cycle. The Genetic Algorithm is used to find the optimal repair rates layout and overhaul moments that provide a minimal expected cost of system operation and maintenance actions and satisfies the desired availability requirement. A numerical example is presented in order to illustrate the approach and the results show that the proposed technique can significantly cut the RAC for machine tools.