In this paper, we focus on the failure analysis of unmanned autonomous swarm (UAS) considering cascading effects. A framework of failure analysis for UAS is proposed. Guided by the framework, the failure analysis of UAS with crash fault agents is performed. Resilience is used to analyze the processes of cascading failure and self-repair of UAS. Through simulation studies, we reveal the pivotal relationship between resilience, the swarm size, and the percentage of failed agents. The simulation results show that the swarm size does not affect the cascading failure process but has much influence on the process of self-repair and the final performance of the swarm. The results also reveal a tipping point exists in the swarm. Mean-while, we get a counter-intuitive result that larger-scale UAS loses more resilience in the case of a small percentage of failed individuals, suggesting that the increasing swarm size does not necessarily lead to high resilience. It is also found that the temporal degree failure strategy performs much more harmfully to the resilience of swarm systems than the random failure. Our work can provide new insights into the mechanisms of swarm collapse, help build more robust UAS, and develop more efficient failure or protection strategies.

Airbag systems are important to a car’s safety protection system. To further improve the reliability of the system, this paper analyzes the failure mechanism of automotive airbag systems and establishes a dynamic fault tree model. The dynamic fault tree model is transformed into a continuous-time Bayesian network by introducing a unit step function and an impulse function, from which the failure probability of the system is calculated. Finally, the system reliability and average life are calculated and analyzed and compared with the sequential binary decision diagram method. The results show that the method can obtain more accurate system reliability and effectively identify the weak parts of the automotive airbag system, to a certain extent compensating for the lack of computational complexity of dynamic Bayesian networks in solving system reliability problems with continuous failure processes.

The metro network plays a vital role in the urban transportation system. However, the metro network is easily damaged by humans and natural disturbances. This can cause serious economic damage, such as the suspension of metro station operations and line disruptions. Therefore, we conducted this study in order to minimize the loss caused by the damage to the metro network and improve the performance of the network after recovery. Based on the cascading failures of metro networks, this paper proposes a recovery model for metro networks considering the value of time. Then, considering the time value, a new node importance measure is proposed using the determination of maintenance priorities. The maintenance priorities of nodes with different importance values are investigated to minimize network losses. Lastly, the applicability of the method is verified by a metro network in Zhengzhou city.

Wind farms have gained wide attention due to unlimited resources and clean energy. Considering that wind turbine systems are always in harsh conditions, subsystem failures could reduce the reliability of wind turbine systems. At present, the maintenance behaviors for wind turbine systems are various (e.g., corrective maintenance, preventive maintenance) when reliability is reduced below the threshold. Considering the maintenance cost and downtime, it is impossible to repair each component in a timely manner. One of the key problems is dividing components into maintenance groups to improve maintenance efficiency. In this paper, a grouping maintenance policy considering the variable cost (GMP-VC) is proposed to improve direct-drive permanent magnet (DPM) turbine systems. Grouping modes are proposed to fully consider the stated transition probability of turbine components and the variable cost of turbine systems. A maintenance model is formulated to select components as members of the group based on a RIM-VC index. An instance is given to verify the proposed GMP-VC method. The result indicates that the proposed maintenance policy may save maintenance costs over baseline plans.

This paper divides the execution process of the command post system into four stages: information acquisition, information processing, decision control and response execution. It combines multilayer complex networks with a phased-mission system. Most studies have only evaluated the reliability of phased-mission systems. This paper evaluates and optimizes the reliability of a phased-mission system. In order to improve the mission success rate and maximize the reliability of a command post system, this paper provides a multitasking node criticality index, and the index is used to identify the key nodes in the command post’s four-stage network Then, the hot backup system of the node is selected to determine the redundant structure of the key node. Under the constraints of the operation and maintenance costs of key nodes, with the goal of maximizing the reliability of the information processing network layer, the multitask redundancy optimization model of each stage is established. Finally, the reliability of the missions before and after redundancy optimization is compared, using the case analysis of the four-layer network to verify the effectiveness of the proposed model.