A multi‐state k ‐out‐of‐ n :F balanced system with a rebalancing mechanism

The balanced system often performs key missions in many fields, such as aerospace, new energy storage, and the military. However, the failure of the system can cause major losses. Therefore, the mission needs to be aborted when the risk of system failure is higher. In this paper, the optimal mission abort policy for a k‐out‐of‐n: F balanced system is studied. The system with a restricted mechanism consists of m sectors and there are n components in each sector, in which the components fail due to two situations: internal failure or external shocks. If the number of working components in m sectors is the same, the system is balanced. If one component fails, the system will force down or resume a component with a probability to keep balance. The model of the mentioned system is established, and the Markov process imbedding approach is used for deriving the reliability indices. Two optimization models are established according to two competitive abort criteria. Finally, an unmanned aerial vehicle performing a mission is discussed to illustrate the proposed mission abort policy.

“…The states after only a one-step transition which may satisfy the abort criteria when deriving MSP and SS are critical. 3 Assume that the state…”

Section: Mission Success Probabilitymentioning

confidence: 99%

Section: Numerical Examplesmentioning

confidence: 99%

Section: Numerical Examplesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Optimal mission abort policy for a balanced system with a restricted mechanism

Dong

2023

Selective maintenance of multi‐state systems with the repairperson fatigue effect and stochastic break duration

Liu

Xiao

Yang

et al. 2022

Systems in the industry are often required to execute a sequence of missions, and the state of the multi‐state systems may deteriorate with the increasing of running time. To improve the system reliability of the next mission, selective maintenance is applied in the limited break. Due to the fatigue effect, the work rate of the repairperson decays with time. However, the existing selective maintenance studies do not consider the impact of fatigue effect on work rate, which leads to underestimating the duration of maintenance actions and overestimating the system reliability. To overcome the above problems, a new selective maintenance model for multi‐state systems is developed to maximize the system reliability of the next mission. First, the decay process of the work rate of a repairperson is described as a piecewise continuous decreasing exponential function of working time. Next, a homogenous discrete‐state continuous‐time Markov process is used to describe the performance capacity degradation of components. Considering the stochastic break duration, based on the state probability distribution of each component obtained by the Markov model, the universal generation function (UGF) method is applied to evaluate the multi‐state system reliability. Then, taking the selected maintenance actions and maintenance priority as decision variables, the resulting selective maintenance optimization problem is solved by a tailored genetic algorithm. Finally, two computational experiments are carried out to demonstrate the effectiveness of the proposed method.

Reliability assessment of two‐level balanced systems with common bus performance sharing subjected to epistemic uncertainty

Tian

Yang

Wang

2023

Two‐level balanced systems with common bus performance sharing (TLBS‐CBPS), represented by power battery systems, play an important role in various industries. Where the two‐level balance refers to when the system is unbalanced, redistributing performance between modules using a common bus for the intermodule balance, followed by the in‐module component performance redistribution to achieve global balance, that is, the performance differences among all components are within the balance degree threshold. However, the component performance has epistemic uncertainty due to measurement limitations, and the distance‐dependent transmission loss incurs during the performance redistribution. These problems make the reliability evaluation of TLBS‐CBPS challenging. To address these issues, a reliability evaluation method for TLBS‐CBPS is proposed by considering two‐level balance, epistemic uncertainty, and transmission loss. First, the system model is constructed from working modes, and the two‐level rebalancing process is described as three nonlinear programming problems. Then, based on the Dempster–Shafer evidence theory, the belief universal generating function (BUGF) method is proposed to evaluate the uncertainty of the system reliability. To improve the computation efficiency, a simplified BUGF algorithm is given through merging, judging, labeling, and expanding realizations. Finally, two examples of lithium‐ion battery packs demonstrate the effectiveness of the proposed method.