Non-intrusive online evaluation of mechanical products is essential for the health monitoring of long-term operating systems. For shaft components, damage simulation provides a direct means to assess online wear degradation without the need for additional sensors. However, simulating randomly accumulated wear is a challenging and time-consuming task, which poses a contradiction for real-time online applications. To address this issue, this paper proposes an online uncertainty quantification method for random wear based on cumulative damage simulation and a self-learning response surface. The accumulated wear damage is first modeled through high-cost model-updating simulations, after which a self-learning response surface—constructed using Generative Adversarial Networks (GAN) and Long Short-Term Memory (LSTM) networks—is developed as a low-cost surrogate to facilitate online wear uncertainty quantification. Finally, the proposed method is validated through a tracked-vehicle drive shaft case study, demonstrating its accuracy, feasibility, and timeliness in monitoring random degradation.

The advent of cyber-physical production systems (CPPSs) has greatly improved production responsiveness. However, effective control and decision-making in CPPSs remain challenging due to the dynamic nature of both internal operations and external environments. We present a multiobjective optimization approach for managing operation, maintenance, and support decisions in CPPSs under time-varying demands. Specifically, a decision-making framework is developed to enable collaborative control, incorporating reliability-based risk assessment and multiobjective optimization techniques. To facilitate continuous decision-making in response to uncertainties, a biobjective optimization model is formulated using a receding horizon control architecture, addressing conflicting objectives simultaneously. An enhanced multiobjective pigeon-inspired optimization algorithm is proposed to generate Pareto-optimal solutions by co-minimizing the production risks and costs. Experimental validations are carried out through both numerical simulations and real-world experiments on a subsea production system in the South China Sea, involving two support sites, six production sites, thirty-six machines, and 288 components.

Efficient real-time decision-making for long-term multiple unmanned aerial vehicles (multi-UAV) missions in geo-distributed environments requires an integrated approach to manage dynamic task demands. We propose a hierarchical dual-layer decision-making framework for multi-UAV mission replanning. The upper layer optimizes multi-UAV deployment using the density-constrained K-medoids clustering and simulated annealing algorithm, achieving globally optimal solutions. The lower layer addresses task assignment via the goal-oriented belief space multiagent reinforcement learning algorithm, which leverages updated belief distributions to mitigate sparse reward and enhance training efficiency. Coordination between the two layers ensures comprehensive coverage of predefined demands while adapting to dynamic events. The effectiveness of the proposed methods is validated through a real-world case study using the 911 call dataset from city emergency services.

High-level decision-making for multiple uncrewed aerial vehicles (multi-UAV) mission planning is crucial, especially with the rising demand for long-term services in geo-distributed environments. However, the interrelated issues of multi-UAV deployment and task allocation are often addressed separately. This article integrates these two problems and introduces a hierarchical framework for effective decision-making. This is achieved by proposing balanced capability (BC), a customized metric tailored for long-term multi-UAV missions with geographically dispersed targets. By considering the global objective and self-organized coordination, a joint optimization model is established from a game-theoretical perspective. Additionally, a novel tangent and cotangent search algorithm (TCSA) is proposed to steer cooperative players toward the global objective in the upper layer, while in the lower layer, a modified distributed task allocation algorithm (MDT2A) incentivizes each autonomous player to efficiently maximize their individual benefits. Simulations validate the effectiveness of the proposed method, with comparative results highlighting the superiority of the algorithms.

Reliability modeling in the context of the Internet of Things (IoT) has received considerable attention from researchers to ensure the safety and stable operation of the relevant systems. However, current methods of reliability modeling are incapable of adequately characterizing the autonomy and cooperativity of the Dynamic Spatiotemporal IoT (DSIoT). To solve this problem, this study proposes an agent-based method of reliability modeling. The authors discuss the autonomy and cooperativity of the DSIoT in case of failure and summarize 11 typical failure states of its functional components. We then develop a multiagent-based framework for the reliability modeling of the DSIoT. Methods of failure modeling are analyzed to describe the propagation of failure within a node and among multiple nodes. Two kinds of criteria of failure are proposed for IoT systems with dynamic spatiotemporal characteristics, and a Monte Carlo simulation-based approach is given to evaluate the reliability of the DSIoT. We also illustrate the uses and effectiveness of the proposed method by using a case study involving a swarm of 16 intelligent unmanned aerial vehicles.

Multiple unmanned aerial vehicle (multi-UAV) system is a type of dynamic spatiotemporal Internet of Things and susceptible to destruction from the external environment. Meanwhile, resilience theory has been introduced to describe the ability of unmanned aerial vehicles (UAVs) faced with disturbances. However, the existing methods do not fully reflect the dynamic spatiotemporal characteristics of multi-UAV systems. Therefore, we proposed a novel resilience metric that integrates mission coverage area and communication status to describe the dynamic spatiotemporal characteristics of multi-UAV systems. On the basis, a combination of importance measures for vulnerability, recoverability, and resilience is presented to support the analysis and identification of weaknesses for the system in whole process. Furthermore, a structure design method is given to improve system resilience by considering both importance measures and trajectory optimization methods simultaneously. Finally, a typical formation topology with six UAVs is simulated as a case study to verify the proposed approach.

Timetabling and scheduling of electric buses (EBs) are crucial for a sustainable city transportation system. Most studies mainly focus on the scheduling and timetabling for one EB line based on the assumption that energy consumption of a battery is linear with distance. In this study, timetabling and scheduling for multiple EB lines are investigated by considering nonlinear energy consumption due to the dynamic load of buses. The optimization objectives are to minimize the number of vehicles and total operation costs, in which the constraints include the limitations of operation range and nonlinear energy consumption of the battery and the capacity of buses can be simultaneously charged at charging stations. Further, an improved particle swarm optimization algorithm is developed to obtain the optimal solution set. Compared with the existing schedule, the proposed schedule can not only reduce the number of vehicles and total charging costs but also distribute the charging periods during off-peak hours to reduce the impact on the urban electricity grid.

The utilization of multiple unmanned aerial vehicles (multi-UAVs) greatly enhances the quality of public services, whereas presents challenges to effective mission planning. Existing research predominantly concentrates on task allocation and path planning, with limited emphasis on integrating UAV deployment and task allocation. Despite the promise of learning-based approaches for joint optimization, their applicability to sim-to-real problems remains restricted. In this paper, we propose a hierarchical decision-making framework empowered by digital twin (DT) technology to enhance resource-efficient utilization and facilitate realtime mission planning. In the upper layer, a swarm of UAVs is divided into subgroups using a K-means method and strategically deployed to corresponding task regions. In the lower layer, a DT-enabled reinforcement learning approach is adopted to leverage real-world experiences. Specifically, we employ a multiagent deep Q learning algorithm to optimize the training process, where each agent interacts with the DT environment through original and target networks to acquire an optimal strategy. The proposed approach is evaluated using simulations that demonstrate its effectiveness in achieving high-quality plans.

The scarcity of time-series data constrains the accuracy of online reliability assessment. Data expansion is the most intuitive way to address this problem. However, conventional small-sample reliability evaluation methods either depend on prior knowledge or are inadequate for time series. This article proposes a novel autoaugmentation network, the worm Wasserstein generative adversarial network, which generates synthetic time-series data that carry realistic intrinsic patterns with the original data and expands a small sample without prior knowledge or hypotheses for reliability evaluation. After verifying the augmentation ability and demonstrating the quality of the generated data by manual datasets, the proposed method is demonstrated with an experimental case: the online reliability assessment of lithium battery cells. Compared with conventional methods, the proposed method accomplished a breakthrough in the online reliability assessment for an extremely small sample of time-series data and provided credible results.

This paper develops a novel approach to addressing the distributed situation awareness (SA) consensus problem for unmanned aerial vehicle (UAV) swarm systems. SA consensus is an important condition for gaining decision-making superiority. However, because of the complexity and antagonism of the mission environment, the widely employed centralized architectures are unable to reach a distributed consensus. We address this urgent issue by proposing a systematic distributed SA consensus scheme, including a distributed optimization-based consensus reaching model, dual-loop decision-making framework, and novel coordination algorithm. Necessarily, we conduct convergence analysis on the proposed algorithm. The effectiveness and superiority of the proposed method are verified by comparative simulations.

Due to the high price of the product and the limitation of laboratory conditions, reliability tests often get a small number of failed samples. If the data are not handled properly, the reliability evaluation results will incur grave errors. In order to solve this problem, this work proposes an artificial intelligence (AI) enhanced reliability assessment methodology by combining Bayesian neural networks (BNNs) and differential evolution (DE) algorithms. First, a single hidden layer BNN model is constructed by fusing small samples and prior information to obtain the 95% confidence interval (CI) of the posterior distribution. Then, the DE algorithm is used to iteratively generate optimal virtual samples based on the 95% CI and small samples trends. A reliability assessment model is reconstructed based on double hidden layers BNN model by combining virtual samples and test samples in the last stage. In order to verify the effectiveness of the proposed method, an accelerated life test (ALT) of the subsurface electronic control unit (S-ECU) was carried out. The verification test results show that the proposed method can accurately evaluate the reliability life of a product. And compared with the two existing methods, the results show that this method can effectively improve the accuracy of the reliability assessment of a test product.

Effective recovery after disruptions is essential to improve system resilience. For many distributed systems such as offshore wind farms and communication networks, spatial location characteristics and limited maintenance resources lead to discontinuous changes in performance during system recovery. However, the stepwise performance is frequently ignored. The coupling relationship between the system and maintenance teams makes it difficult to improve the system resilience from the recovery perspective. In this article, a new method that combines the resilience importance measure and enhanced pigeon-inspired optimization (PIO) is presented to optimize the system resilience under stepwise recovery conditions. First, this study proposes a resilience-oriented importance measure that evaluates the recovery priority for each component. Second, an optimization model is established to minimize the system resilience loss by joint optimization of recovery sequence and task assignment under the conditions of multiple maintenance teams. Third, the resilience importance measure is combined with roulette wheel selection to form a high-quality intitle swarm of PIO. Fourth, an importance measure-based PIO with a Gaussian mutation and adaptive crossover operator is designed to find an optimization solution for system resilience. Finally, the recovery of a network system consisting of 32 nodes and 71 edges is studied. Compared with the stochastic method and traditional PIO, the proposed method in this study reduces the system resilience loss by 48.87 and 15.54%, respectively.

Fault prognosis based on single model is generally inaccurate due to the varying working conditions. A multistage fault prognosis methodology combining stage identification with Bayesian networks (BNs) and time series approach with particular emphasis on the autoregressive moving average (ARMA) model is proposed to solve this problem. In the first stage, degradation data are identified, and outliers are marked by the Euclidean distance. Degenerate attributes of outliers are finely identified by BNs and matched to the corresponding model. In the second stage, the ARMA model is used for prognosis according to the results of the fine identification. Subsequently, the double-precision identification and ARMA submodel prognosis are carried out alternately throughout the prognosis process. Three degradation types of permanent magnet synchronous motor are simulated to verify the applicability of the method. Result shows that it can track the changes in the degradation in time and obtains better results.

Preventive maintenance (PM) activities in battery energy storage systems (BESSs) aim to achieve a better status in long-term operation. In this article, we develop a reinforcement learning-based PM method for the optimal PM management of BESSs equipped with prognostics and health management capabilities. A multilevel PM framework is established to generate a PM action strategy considering costs, capacity, and reliability simultaneously. Finite costs are the constraints, and reliability is the objective according to capacity degradation, respectively. The proposed PM agent with an integrated Monte Carlo tree search and a deep neural network (DNN) utilizes the state-of-health information of large-scale batteries in the BESS and selects optimal maintenance actions. The DNN is used as the state-action value function to extend the ability to address PM problems with large state-action spaces. The case of a BESS with 9 × 12 × 4 batteries in a fleet is simulated via Python. The results show that the PM agent can achieve efficient and steady decision-making proficiency in BESS PM management.