Availability for multi-component k-out-of-n: G warm-standby system in series with shut-off rule of suspended animation. (May 2023)

• Record Type: Journal Article
• Authors: Guo, Linhan; Li, Ruiyang; Wang, Yu; Yang, Jun; Liu, Yu; Chen, Yiming; Zhang, Jianguo
• Is Part Of: Reliability engineering & system safety. Volume 233(2023)
• Publication Date: 2023-05
• Subjects: Availability -- Continuous-time Markov chains (CTMC) -- Multi-component k-out-of-n: G system in series -- Suspended animation (SA); Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2023.109106
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: An availability model on the multi-component k -out-of- n : g warm-standby system with SA is constructed. An effective algorithm for the transition rates of the SA states is presented. The solved availability is accurate compared without considering the SA. The system availability is proven to equal the operation state probability of any one of the subsystems. Abstract: Suspended animation (SA) indicates that a failed subsystem in a series system causes the others to stop working. If the SA is neglected, the system availability will be underestimated. Compared with previous research about SA only for a k -out-of- n : G system or a multi-component series system, the proposed availability model focuses on the multi-component series system composed of different k -out-of- n : G warm-standby subsystems considering the SA rule, in which a group of CTMC (Continuous-time Markov chains) including the states of operation, SA, and malfunction lies on the subsystem level are constructed to avoid the state explosion effectively. Then an effective algorithm for the transition rates of the SA state is constructed to solve the three types of state probabilities, whose iteration begins with the same subsystem CTMC not considering the SA rule. The metrics on reliability and availability are derived according to the relationships of the state probabilities. Some important properties of the availability and SA are proven. Actual engineering data is utilized to verify the model accuracy and algorithm efficiency. The more complex situation is confirmed by the simulation. Numerical examples of the ATE illustrate the versatility of the method by sensitivity analysis.