Premium
Efficient reliability analysis of dynamic k ‐out‐of‐ n phase‐AND mission systems
Author(s) -
Wang Chaonan,
Hu Yuliang,
Xing Liudong,
Guan Quanlong,
Yang Chunhui,
Yu Min
Publication year - 2021
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.2827
Subject(s) - reliability (semiconductor) , task (project management) , component (thermodynamics) , computer science , phase (matter) , node (physics) , reliability engineering , wireless sensor network , real time computing , algorithm , engineering , systems engineering , computer network , power (physics) , chemistry , physics , organic chemistry , structural engineering , quantum mechanics , thermodynamics
Motivated by real‐world applications of satellites and wireless sensor networks, this paper models and evaluates a dynamic k ‐out‐of‐ n phase‐AND mission system ( k/n ‐PAMS). The mission task conducted by a k/n ‐PAMS involves multiple consecutive phases; the mission is successful as long as the task is successful in any of the phases. Due to factors, such as scheduled maintenance, location changes in task execution during different phases, and resource sharing with other tasks, the total number of available components n for the considered mission task and the required number of working components k may change from phase to phase. In addition, due to varying load and working environments, component failure time distributions are also phase dependent. This paper proposes an analytical modeling approach based on multivalued decision diagrams (MDDs) for assessing reliability of the considered k/n ‐PAMS. The approach encompasses a new and fast MDD model generation algorithm that considers behaviors of all the mission phases simultaneously based on node labeling. As demonstrated through empirical studies on k/n ‐PAMSs with different sizes (different numbers of phases and different numbers of system components), the proposed algorithm is more efficient than the traditional phase‐by‐phase model generation method.