With the acceleration of urbanisation, the problem of ground settlement in tunnel construction has become a serious challenge. Aiming at the existing ground settlement monitoring and resilience management problems such as limited monitoring range, neglected spatio-temporal characteristics, and poor combination of prediction results and resilience management, this paper proposes a two-stage spatio-temporal prediction and resilience optimization for tunnel-induced ground settlement. Specifically, firstly, this paper constructs a comprehensive monitoring architecture integrating SBAS-InSAR technology and Internet of Things (IoT) technology, which realises accurate and comprehensive monitoring of ground settlement. Secondly, a two-stage spatio-temporal prediction method of ground settlement is proposed: based on the wide-area spatio-temporal data acquired by SBAS-InSAR technology, the spatio-temporal transformer model is used to make the preliminary prediction. Then, with the small-area variables collected by IoT, the parameter-seeking optimisation algorithm based on the Grid Search-Particle Swarm is used in conjunction with the Time Convolutional-Bidirectional Long and Short-Term Memory Network (GR-PSO-TCN-BiLSTM) model to correct the prediction error. In terms of resilience optimisation, this paper proposes a multi-stage resilience enhancement strategy based on ground settlement prediction, which combines prevention importance, degradation importance and recovery importance, aiming to maximise the resilience of tunnel-induced ground settlement area. Finally, an empirical analysis using the traffic along the Zhengzhou Metro as an example verifies the effectiveness of the proposed method. These results indicate that coupling wide-area remote sensing with local IoT correction can substantially improve settlement prediction accuracy and provide actionable guidance for maintenance prioritization, thereby enhancing the robustness and recovery capability of metro systems.

Amid increasing extreme rainfall events, urban pluvial flooding poses a significant threat to city infrastructure and public safety. To address the limitations such as poor coordination between subsystems and low resilience of conventional IoT frameworks in underground drainage networks, this paper proposes a resilience-driven architecture termed R⁴-UDN. The framework integrates four signature mechanisms: event-triggered sensing, resimulation-in-loop, cascade-aware control, and cross-layer KPI alignment, forming a closed-loop management system. The paper introduces a multi-stage resilience index and embeds it into a semi-Markov cascade model to capture system performance dynamically. Furthermore, it optimizes the resilience and sequence-sensitive cost. A case from Chengdu, China, is studied to confirm that the proposed method significantly enhances resilience compared to conventional strategies, validating the practical value of R⁴-UDN for enhancing the resilience of sponge city underground drainage systems.

Digital twin with generative artificial intelligence (AI)-enabled maintenance optimization serves as an essential foundation for the performance of intelligent manufacturing systems (IMS). However, existing models often fail to simultaneously consider both reliability and cost. In an IMS, reliability guarantees stable system operation and consistent product quality, while cost control enables enterprises to optimize resource use, enhance productivity, and lower operating costs. Together, these metrics determine the overall effectiveness of the system and the competitiveness of the enterprise. To address the research gap, this study proposes a maintenance optimization method that jointly considers reliability and cost. In particular, a novel reliability assessment method is developed, incorporating both physical failures modeled and functional outputs that account for imperfect quality inspection. Moreover, considering rework and imperfect quality inspection, a cost analysis is performed for various operation modes of IMS. Further, a novel adaptive multi-objective particle swarm optimization with maintenance priority constraints (AMOPSO-P) method is developed to conduct the IMS control decision-making process, optimizing reliability and cost. Finally, to validate the proposed algorithm, we conduct a case study of China United Equipment Group on control decisions for a three-stage, four-station servo valve manufacturing system using simulations.

Multiple autonomous underwater vehicles (AUVs) are being developed to survey marine ecosystems. The main challenge lies in that most of the research focuses on the physical layer of AUVs, but less on the data layer which could contribute significantly to the performance of AUVs. Moreover, there is a lack of shock prevention and post-shock recovery strategies for AUVs. Therefore, to comprehensively analyze the multi-level performance changes of the multiple AUVs, this paper models the physical layer and data layer of the AUVs within the Internet of Underwater Things (IoUT), and the architecture of the corresponding system components based on digital twin technology. After that, we model the IoT data reliability in AUVS. Then, the physical performance evaluation is carried out for different phases of the performance change process in AUVs. To enhance the ability of the multiple AUVs to resist external interference, a multi-stage control scheme is proposed. At last, compared with the generalized scheme, a case simulation shows that the proposed scheme can maximize the protection against external interference. The control scheme leads multiple AUVs to a 6.13% improvement in performance efficiency, the data reliability of 49.66% and 10.95% cost savings in multiple AUVs.

As the environment deteriorates, natural disasters occur more frequently and become more devastating to human beings and the environment. After a disaster, to quickly and optimally restore the damaged things, including physical systems (e.g., transport networks) and the environment, needs decision makers to own sufficient data/information. Unmanned vehicle detection systems (UVDS) are undoubtedly feasible tools in collecting such data in a harsh environment. The most important challenges in UVDS management are on modeling the UVDS data layer and multi-stage recovery strategies, which have received little research. To address such problems, this paper proposes a multi-stage control strategy for UVDS based on Internet of Things (IoT). The optimal decision is decided by utilizing four indicators: performance recovery efficiency, normal detection probability, operation cost, and economic benefit cost, respectively. The simulation results show that the proposed strategy improves the performance recovery efficiency by 12.1% and the normal detection probability by 3.9%, the operation cost declines by 58.4%, and the economic benefit cost by 75.9% compared with the general control strategy.

Due to being nonpolluting and renewable, intelligent solar photovoltaic (PV) technology is widely used to provide electricity and becomes a cornerstone to sustainable energy and smart energy management. Different from existing studies that improve the PV efficiency by changing cell materials, this article proposes a novel system reliability and cost model of enhancing the PV power resilience performance from the perspective of optimizing the number of PV panels. Specifically, a multiobjective planning model is proposed, which determines the optimum number of spare parts for PV panels maximizing the output power resilience while maximizing the system reliability and minimizing the cost. The reliability measures the probability of stable operation of a PV panel considering the no-power output state. The cost factor encompasses negative cost of environmental benefits, resource cost, operation and maintenance cost, and penalty cost. Experiments are performed on fifty sets of Pareto optimal solutions in summer and winter cases to illustrate effectiveness of the proposed method by using a ground-mounted PV project in Zhongwei City, China.

Offshore wind farm is one of the most promising applications in the Internet of Things (IoT), due to being energy-renewable and resources-unlimited. However, the reliability monitoring and maintenance models of power equipment based on communication paths and sensors are still immature in the smart offshore wind farm (SOWF). Based on the hierarchical architecture and end-to-end communication, a dynamic reliability assessment model (DRAM) is proposed for SOWFs. First, based on the IoT hierarchy, a four-stage network is developed to represent the relationship or dependencies between diverse devices in a complex SOWF. Second, a two-layer DRAM with forward monitoring (FM) and lateral protection (LP) is proposed. The FM encompasses a sensor network-based state-monitoring phase (monitoring weather data like temperature and wind speed), and a data-monitoring phase (monitoring the reliability-related data like reception power and data processing speed). The LP includes a signal-protection mode (LP-I) ensuring that virtual machines read the data and issue protection orders before turbine failures to minimize losses, and a radius-maintenance model (LP-II) performing maintenance of the failed turbine nodes. Simulation results show that the optimal maintenance strategy based on DRAM outperforms the benchmark maintenance method for traditional wind grids.

With the continuous development of underground space, the vigorous development of underground rail transport has become an effective way to relieve the pressure of urban traffic. However, ground settlement caused by tunnels can lead to cracks, settlements, and collapses of nearby buildings, resulting in serious economic losses. To solve the problems of poor generalization performance of risk warning models and uneven allocation of maintenance resources in PHM for ground settlement in existing studies, an Internet of Things (IoT)-enabled risk warning and maintenance strategy optimization method is proposed in this paper. In risk warning, firstly, a weight optimization method with the decision objectives of variance maximization, correlation minimization, and estimation error minimization is introduced to find the optimal weights of the base learners in ensemble learning prediction. Secondly, a one-dimensional convolutional neural network-bidirectional long and short-term memory network (1D CNN-BiLSTM) is used to make further predictions on the prediction residuals. In maintenance strategy optimization, threshold-based optimization and cost-based risk-importance maintenance strategies are proposed based on the risk warning results of ground settlement. To test the enhanced effectiveness of the proposed method, a set of comprehensive simulations is carried out in Ningbo city rail transit as an example. The results show that the proposed risk warning method has smaller MAE, MAPE, and RMSE compared to other baseline methods. In addition, the proposed maintenance strategy reduces 12.5%, 16.3%, and 85.7% in terms of cost compared to the baseline method. Overall, the simulation results confirm the advantages of the proposed framework for IoT-enabled risk warning and maintenance strategy optimization in PHM.

Advanced Internet of Things (IoT) technology has a profound impact on improving the intelligence level of intelligent transportation systems (ITS) and promoting the sustainable development of urban transportation. However, how to use IoT to process traffic flow and make ITS develop towards automation and global control is still a challenge. Against this backdrop, a prospective traffic controlling model is proposed for ITS based on IoT to enhance the awareness of roads and the responsiveness of transportation system. When traffic congestion events occur, ITS can provide the optimal control strategy of vehicle-to-everything supported vehicles (V2X-supported vehicles) from a macro perspective to control the traffic flow globally and improve traffic efficiency. Specially, the optimal control strategies consider the potential congested road segments caused by congestion propagation. Meanwhile, this paper explores the impact of route choice behavior of V2X-supported vehicles on system performance. The simulation results show the optimal control strategies can alleviate congestion effectively and improve transportation system performance significantly by controlling vehicles.

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.

With the application of the Internet of Things (IoT), smart charging piles, which are important facilities for new energy electric vehicles (NEVs), have become an important part of the smart grid. Since the smart charging piles are generally deployed in complex environments and prone to failure, it is significant to perform efficient fault diagnosis and timely maintenance for them. One of the key problems to be solved is how to conduct fault prediction based on limited data collected through IoT in the early stage and develop reasonable preventive maintenance strategies. In this article, a real-time fault prediction method combining cost-sensitive logistic regression (CS-LR) and cost-sensitive support vector machine classification (CS-SVM) is proposed. CS-LR is first used to classify the fault data of smart charging piles, then the CS-SVM is adopted to predict the faults based on the classified data. The feasibility of the proposed model is illustrated through the case study on fault prediction of real-world smart charging piles. To demonstrate the advantage in prediction accuracy, the proposed fault prediction model is compared with the classic baseline models, such as LR, SVM, decision tree (DT),$K$-nearest neighbor (KNN), and backpropagation neural network (BPNN). Finally, based on the proposed fault prediction method, preventive maintenance based on a probability threshold with the minimum total expected cost is proposed. Simulation results show that the proposed maintenance strategy has a better performance in reducing the total maintenance cost compared with traditional periodic maintenance. This is valuable for the development of preventive maintenance strategies for repairable systems under early real-time monitoring data.

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.

Importance measures in reliability engineering are used to identify weak components of a system and signify the roles of components in contributing to proper functioning of the system. Recently, an integrated importance measure (IIM) has been proposed to evaluate how the transition of component states affects the system performance based on the probability distributions and transition rates of component states. In the system operation phase, the bathtub curve presents the change of the transition rate of component states with time, which can be described by three different Weibull distributions. The behavior of a system under such distributions can be modeled by the semi-Markov process. So, based on the reported IIM equations of component states, this paper studies how the transition of component states affects system performance under the semi-Markov process. This measure can provide useful information for preventive actions (such as monitoring enhancement, construction improvement, etc.), and provide support to improve system performance. Finally, a simple numerical example is presented to illustrate the utilization of the proposed method.

Importance measures are widely used to characterize the roles of components in systems. The system lifetime can be divided into different life stages. Traditionally, importance measures do not consider the possible effect of the expected number of component failures over a system's lifetime and over different life stages, which, however, has a great effect on the system performance changes, and should therefore be taken into consideration. This paper extends the integrated importance measure (IIM) from unit time to system lifetime, and to different life stages. Based on the renewal functions of components, this measure can evaluate the changes of the system performance due to component failures. This generalization of the IIM describes which component is the most important to improve the performance of the system during the system lifetime and at different life stages. An example of the application of an oil transportation system is presented to illustrate the use of the generalized IIM.

This paper mainly focuses on the integrated importance measure (IIM) of component states for maintenance processes. To describe the impact of each component state in maintenance processes, a maintenance cost function of multi-state systems is defined at first. Second, considering the probability distributions, transition rates of the component states, and system maintenance costs, the IIM of component states is described. The corresponding characteristics of the IIM of the component states are discussed in both series systems and parallel systems. Then the relationships between IIM and Griffith importance, Wu importance, mean absolute deviation, and multi-state redundancy importance measures are also discussed. At last, a numerical example is given to demonstrate the IIM of component states. The results show that IIM can be used to identify the most important component state for the maintenance decision.

This paper mainly focuses on the integrated importance measure (IIM) of component states based on loss of system performance. To describe the impact of each component state, we first introduce the performance function of the multi-state system. Then, we present the definition of IIM of component states. We demonstrate its corresponding physical meaning, and then analyze the relationships between IIM and Griffith importance, Wu importance, and Natvig importance. Secondly, we present the evaluation method of IIM for multi-state systems. Thirdly, the characteristics of IIM of component states are discussed. Finally, we demonstrate a numerical example, and an application to an offshore oil and gas production system for IIM to verify the proposed method. The results show that 1) the IIM of component states concerns not only the probability distributions and transition intensities of the states of the object component, but also the change in the system performance under the change of the state distribution of the object component; and 2) IIM can be used to identify the key state of a component that affects the system performance most.