Reliability assessment is essential for ensuring the stable operation of industrial systems. This study presents a hybrid reliability assessment framework that integrates mechanism-based and data-driven approaches for online degradation evaluation. A representative degradation mode— impact fatigue—is investigated to demonstrate the method. The mechanistic component is formulated by using crack propagation theory and Miner’s cumulative damage rule; while the data-driven one employs a generalized power-law Wiener process with an additive nonlinear drift. Both sources of information are fused through an evolving unscented Kalman filter that performs optimal and unbiased online state estimation via prediction and observation updates. In the proposed framework, the Wiener process serves as the state equation, and the mechanistic model functions as the measurement equation. The reliability of impact fatigue is further evaluated by using the inverse Gaussian distribution, which is inherently associated with the Wiener process. Application to a thrust bearing in the main pump of a pressurized water reactor demonstrates the effectiveness and superiority of the proposed method over existing ones.

Cooling water intake systems in nuclear power plants are increasingly susceptible to operational disruptions due to marine organism intrusions. Traditional optimization methods based on static numerical simulations and evolutionary algorithms lack adaptability to complex, dynamic operational conditions and uncertainties. This study introduces a cognitive intelligence-integrated optimization framework, combining discrete element method (DEM) and finite element method (FEM) simulations with deep reinforcement learning (DRL), fuzzy analytic hierarchy process, and expert knowledge to optimize filtration net parameters adaptively and robustly.

The cascade filtration system of nuclear power plants has long faced challenges such as high operational uncertainty and complex human decision-making in response to the interception of marine hazards. In order to break through the dual limitations of the lack of real-time performance of traditional physical models and the lack of interpretability of data-driven methods, this study proposes ' CogFiltrate ' -a dynamic decision-making framework based on human-machine collaborative cognition. The framework pioneered the intelligent operation and maintenance paradigm of deep integration of fuzzy reasoning and uncertainty analysis : by constructing a fuzzy evidence network to uniformly quantify the randomness and cognitive uncertainty factors; on this basis, a two-way confidence negotiation mechanism is developed to achieve collaborative optimization of artificial intelligence recommendations and artificial experience in dynamic risk scenarios. After the system is deployed on the full-scale filtering platform of the nuclear power plant, the emergency response efficiency is significantly improved, and the reliability of the framework is successfully verified under extreme disaster conditions. This study provides an example of combining theoretical innovation with engineering practice for the intelligent operation and maintenance of critical infrastructure, and opens up a new path for cognitive intelligence to drive industrial safety.

Existing studies on traffic flow prediction primarily rely on on-board devices to collect vehicle trajectory data , which can potentially infringe upon the privacy of users and limit the applicability of the method. Additionally, traffic flow prediction remains challenging due to the complex spatial and temporal dependencies within real-world traffic networks. To address these limitations, this paper introduces a framework for analyzing discrete vehicle trajectory data at urban intersections. By incorporating various external physical factors into traffic flow prediction, this framework derives embedding vectors from vehicle trajectory sequences and road network topology , modeling their spatio-temporal dependencies using Skip-Gram and GraphSAGE, respectively. Additionally, the intersection similarity is introduced to capture and integrate traffic flow patterns between the target intersection and similar intersections. A Spatio-Temporal Graph Convolutional Neural Network (ST-GCN) algorithm, which combines Graph Convolutional Networks (GCN) with Long Short-Term Memory (LSTM), is developed to achieve precise traffic flow prediction. Extensive experiments on a real-world traffic flow dataset from Qingdao, China, validate that the proposed method outperforms state-of-the-art baseline methods .

Industrial Internet of Things (IIoT) plays a crucial role in advancing smart manufacturing by connecting numerous devices, enabling data exchanges, and supporting industrial applications. Yet, the timely and proper scheduling of asynchronously generated Heterogeneous Computing Tasks (HCTs) in IIoT environments remains a significant challenge. In this article, we first introduce the representation and notation of such HCTs and define a computing network structure. We then propose an initial mathematical programming-based scheduling model aimed at minimizing HCT completion time. To make this model easy to solve, we reformulate it by using logical constraints and derive a constraint programming-based model, for which a feasibility-guaranteed solution algorithm is developed. This algorithm leverages two easily-verified propositions to either identify feasible solutions or demonstrate the infeasibility of the problem.Furthermore, we have proven a critical proposition that facilitates the development of a hybrid programming-based scheduling approach, effectively combining the strengths of both mathematical and constraint programming models. As demonstrated through extensive computational experiments, our proposed approach achieves an average reduction of 20% in HCT completion time in comparison with its existing peers. It consistently and timely provides the high-quality solutions that meet the required deadlines.

Artificial intelligence-generated content (AIGC) is increasingly featuring a key to extract intent information from external instructions and generate required content in digital twin (DT)-enabled application scenarios. To construct DT contexts as the input of generative artificial intelligence (GenAI) algorithms, space–air–ground integrated networks (SAGINs) with hierarchical structures can facilitate object cloning from the physical world to a virtual space within vast geographical regions. In this work, we propose a novel DT synchronization framework residing in SAGINs to provide AIGC services. Autonomous aerial vehicles (AAVs) are in charge of gathering real-time information from the external environment and transmitting synchronization data to the core cloud via a communication relay, i.e., base station (BS) or satellite. In the proposed framework, we develop a resource allocation problem for DT synchronization, aiming to minimize the time-average energy costs of AAVs under the constraints on resource provision and long-term transmission queue stability. To address the complexity and dynamics of SAGINs, we first transform the original resource allocation problem into several deterministic problems based on the Lyapunov optimization. Then, a diffusion model-based resource allocation (DRA) algorithm is developed to solve the deterministic problem in each time slot, where a novel diffusion model is proposed to generate integer relay selection decisions with the aid of auxiliary gradients provided by conventional model-based optimization. Finally, we provide theoretical and simulation evaluations to demonstrate that the DRA algorithm can reduce energy consumption and improve resource utilization by comparing it with deep reinforcement learning (DRL) and heuristic algorithms.

As a critical component in the Internet of Things (IoT) ecosystem, unmanned aerial vehicles (UAVs) play a pivotal role in low-altitude economy applications, where flight safety is essential for reliable and efficient operations. Although significant progress has been made in UAV intelligent fault diagnosis, it is challenging to effectively address the noise issues in complex flight environments, especially when sample noise and label noise coexist. To this end, this paper first investigates the sample-label noise coupling fault diagnosis (SLNFD) problem in UAVs, and proposes a novel two-stage framework called Learnable Wavelet Packet Transform and Scale Graph Label Enhancement (LWPT-SGLE). Specifically, the LWPT subnetwork integrates wavelet packet transform and deep learning models, and a convolutional layer is employed to replace the traditional filter component to adaptively learn denoising parameters for efficient noisy sample reconstruction. The SGLE subnetwork is designed for label noise, which extracts multi-scale data embeddings, identifies clean samples using a small loss criterion, and applies graph embedding learning to relabel the remaining samples. Comprehensive experiments on the RflyMAD dataset demonstrate the superiority of the proposed framework, achieving outstanding denoising performance, with a reconstructed signalR² reaching 0.9974, and outperforming existing methods by at least 7% when handling label noise.

Human-cyber-physical system has emerged as a pivotal tool for intelligent production throughout the whole lifecycle. In this paper, a health state assessment approach based on hierarchical data fusion is proposed under human-cyber-physical system architecture. The human-cyber-physical system emphasizes the flexibility of dynamic operating states and the evolvability of the enabling models. First of all, a multiple hierarchy consisting of equipment, systems and function is established as a physical basis for human-cyber-physical system. Regarding the equipment-level assessment characterized by extensive input parameters, a dynamic weighting method is exploited to highlight the abnormal parameters. Thus, the degraded device can be significantly reflected in the system-level assessment by fusing equipment parameters. To evaluate the operation function of a plant, a Nearest Class Mean classifiers-based Random Forests algorithm is explored considering the small-scale and imbalanced instances in different health levels. However, the conventional fixed and changeable models threaten the accuracy and rationality of the human-cyber-physical system. We introduce incremental learning to reform Random Forests that can update promptly with new data and knowledge. Finally, the proposed scheme and technologies are applied in a nuclear power plant to demonstrate their effectiveness.Note to Practitioners—The modern production desires to be further intelligent for improvement of economic benefits but with the consistent requirements of high safety and reliability. The trades-off between efficiency and safety bring the greater challenges to the operation and maintenance for production. This paper proposes an evolvable human-cyber-physical system scheme that not only integrates more intelligent elements but also attaches some solution to the abovementioned problem. The scheme is implemented on the multi-hierarchy health states assessment which is the core in production maintenance against lagging. This equipment-system-function fusion and assessment provide a unified platform for multi-departments to collaborate and self-manage. The novel weighting method abandons the conventional subjective evaluation criteria with high time consumption. Computers are convenient to possess sensing data efficiently and highlight the anomaly obviously. In particular, it is mentioned that the intelligent models in human-cyber-physical systems are always fixed and unable to alter with the physical system changes. Incremental learning, a promising branch of machine learning, is introduced to reform the assessment model to be stainable developed with new data adding. The scheme takes a nuclear plant as example because it is more corresponding to these issues. The future studies can extend the scheme to other scenarios.

Industry 5.0 heralds a new wave of the industrial revolution, placing a spotlight on human-centric intelligent manufacturing. At the core of Industry 5.0 lies the human-cyber-physical system (HCPS), a composite intelligent system where interactions among humans, cyberspace, and physical assets are orchestrated across diverse manufacturing levels and phases. Understanding the pivotal roles played by humans in these advanced systems is of paramount importance. Nonetheless, the exploration of HCPS within the context of Industry 5.0 remains in its infancy. This paper presents a holistic literature review of industrial HCPS from a human-centric perspective. A united architecture is employed to encompass the aspects of cognitive-to-technology integration and human-to-human interaction in HCPS, highlighting human-in-the-loop, human-on-the-loop, and human-in-the-society paradigms. The mechanisms of these paradigms and their effects on design, production, and service are investigated to expand the research landscape of intelligent manufacturing in Industry 5.0. Key enabling technologies that facilitate harmonious tri-space integration are introduced, and the future challenges of industrial HCPS are discussed. This work is expected to attract more open discussions and in-depth research on HCPS in the new industrial revolution era.Note to Practitioners—This paper is motivated by the emergence of Industry 5.0 that integrates humans into cyber-physical systems to offset drawbacks on both sides. It presents an overview of HCPS-related works to identify the state-of-the-art and open problems in the Industry 5.0 era. The review of HCPS applications in the design, production, and service phases can benefit engineers in the intelligent manufacturing area. Key enabling technologies on human ability augmentation, human-robot interaction, digital twin, human-cyber-physical data fusion, crowdsourcing, and system modeling, are analyzed to attract researchers in broader research fields to join in the development of industrial HCPS.

Generative methods for creating computer-aided design (CAD) models have gained significant attention over the past two years. However, existing methods lack fine-grained control over the generated CAD models, making it difficult to manage details such as model dimensions and the relative structure of components. To address these limitations, this study introduces Diffusion-CAD, a diffusion-based generative approach that outputs CAD construction sequences. Diffusion-CAD iteratively denoises Gaussian noise into continuous CAD vectors, which are then transformed into discrete CAD sequences. We designed classifier-free and classifier-guided methods to control the distribution of Gaussian noise, CAD sequences, and noisy CAD vectors separately, thereby achieving a variety of fine-grained control tasks. Extensive experiments demonstrated the superior performance and novel capabilities of the proposed method for conditional generation tasks.

Collaboration with robots through physical contact offers a more intuitive, natural, and engaging operational experience, showcasing vast potential in the field of human-robot interaction. However, current physical interaction devices, such as collaborative robots and haptic feedback mechanisms, are limited by their singular modes of motion and feedback, hindering enhancements in interaction experiences. Herein, we present a multistable soft actuator capable of driving multimodal shape changes and passively conforming to user touch. This actuator can memorize and maintains any deformation with zero power consumption. Its structural mechanical properties can be dynamically adjusted to produce rich haptic feedback for the user, including changes in shape, elasticity, stiffness, and even sensations of rupture and weightlessness. Structurally, the mechanism consists of a network of pneumatic bistable units in series and parallel configurations, which can switch states under air pressure or external force, achieving extension, contraction, and omnidirectional bending. The input of air pressure can either impede or assist deformation, altering structural stiffness and resulting in varied loading curves. With its high safety in physical interactions, robust operability, and rich mechanical tactile feedback, the multistable soft actuator promises new design directions for physical human-robot interaction devices.

Industrial Internet of Things (IIoT) is changing the way in which factories operate with the help of various industrial applications. However, the execution uncertainty of computing tasks has always been ignored in IIoT applications. In this article, we define a novel conditional task graph to describe the execution uncertainty and present a generation algorithm to obtain all task scenario graphs and corresponding occurrence probabilities. Then, a new IIoT-oriented multitask scheduling model under execution uncertainty is built. This model is simplified by reformulating the nonlinear constraints and subsequently decomposed into several small-scale models using the Lagrange multipliers, from which a decomposition-based algorithm is derived to solve the decomposed small-scale models and progressively acquire a well-optimized solution of the initial model. Furthermore, a patching algorithm is constructed to improve the obtained solution. Finally, many test cases are generated, and four selected algorithms are taken for comparison to evaluate the performance of our algorithms. The results demonstrate that our algorithms remarkably outperform the others. Besides, the solutions of the proposed algorithms can completely satisfy the execution deadline constraints of different task scenarios.

Planning a disassembly sequence of product structure in a remanufacturing-oriented manner is a newly emerging and important problem, which has attracted considerable attention owing to high demand for sustainable development. However, current approaches cannot lead to a reliable disassembly plan focused on the product structure itself, starting from functional properties to application demand stimulated by remanufacturing. First, they fail to focus on the role that EOL products play in the promotion of remanufacturing. Second, determining a disassembly solution for advanced and sophisticated products is complicated and time-consuming. Third, the uncertainty of mixed information triggered by the intercrossing of multiple subjects is neglected. In this study, an improved DSP method is first proposed by integrating the remanufacturing properties of EOL products, an improved QICA, and the measure and update of the reliability of experts’ decision-making to perform DSP. The functional recoverability of EOL product parts is marked to identify the remanufacturing value of a disassembly plan. The expected reproduction probability is adopted to increase the population diversity and improve the ability of QICA to search for feasible solutions. The model and update mechanism of experts’ recognition degree are constructed to narrow the score deviation. Finally, we provide a case for a sewing machine to verify the superiority of the improved QICA. The results show that it is capable of obtaining satisfactory solutions and outperforms the quantum genetic algorithm and particle swarm optimizer.

To confront the great challenge of industrial big data, the software-defined industrial networks (SDINs) are introduced to dynamically coordinate these data flows among the heterogeneous and distributed computing resources. Deciding how to more efficiently schedule many homogeneous computing tasks, which extensively appear in SDIN, becomes of critical importance. To this end, this article first illustrates some related notations and assumptions of the homogeneous computing tasks and computing networks, from which a new targeted optimization model is formulated. Then, the model is significantly enhanced by reformulating all the nonlinear constraints and inventively establishing the symmetry-breaking constraints and computation time cuts. Furthermore, considering the computational complexity, the scheduling process with many homogeneous computing tasks is further viewed as two associated phases: computing nodes assignment and tasks sequencing. As a result, a novel bilevel decomposition algorithm is proposed using Lagrangian decomposition and a new form of Lagrangian relaxation. Finally, a real industrial scenario is chosen, and three comparison algorithms are used to demonstrate the preponderant performance of the proposed algorithm.

Successful product development relies on the enterprise system (ES) to manage product lifecycle data and support decision-making in various levels. Since the amount of data generated in the industrial Internet of Things is increasing dramatically, new paradigms of the ES are required to realize the distributed intelligence. Recent progress in edge computing has enabled advances in decentralized decision support systems. Although the product design is a key stage of new product development, none of the recent studies in edge-based ES have shed light on this stage. Therefore, an edge-based ES framework for design scheme evaluation is proposed in this article. It not only accomplishes most of the decision-making tasks within the edge mesh of an enterprise, but also solves the information island of multiple decision makers from different geographical regions. Moreover, a multigroup decision-making algorithm is proposed to enable collaborative design scheme evaluation. The evaluation processes of designers, experts, and customers are analyzed systematically in this article. For one advantage, the trapezium cloud model is applied to convert the qualitative evaluation information given by designers and experts into qualitative values. It decreases the cognitive discrepancy and solves the information distortion. For another, EEG data are utilized to explore the implicit psychological states of customers during the product operation. All the evaluation results of multiple groups of decision makers are integrated by the fuzzy measure and Choquet integral to determine the optimal design scheme. A case study is conducted to illustrate the feasibility of the method proposed in this article.

With the expansion of the sharing economy, growing urban traffic, and increasing environmental pollution, bike-sharing systems (BSSs) are developing rapidly all over the world. A major operational issue in BSS is to reposition the bikes over time such that enough bikes and open parking slots are available to users. Especially during peak hours, it is essential to stabilize BSS in use. To cope with the issue, this article proposes a new approach integrating multiobjective optimization and a weighting factor based on the shortage event types of each station. In addition, the multiobjective artificial bee colony algorithm is modified according to the features of this work to find optimal solutions. The proposed approach is applied to the real-life repositioning of a BSS during peak hours to verify its feasibility and effectiveness. Also, the algorithm is compared with other frequently used multiobjective algorithms. For the comparative study, convergence metric and spacing are adopted to further measure the algorithm performance. The scalability of the proposed approach in addressing the multiobjective repositioning problems during peak hours is also verified by multiple trials. Note to Practitioners-This work deals with bike repositioning in bike-sharing systems (BSSs) during peak hours, which has major significance in the efficient operation of such systems. It builds a multiobjective optimization model and solves it through a modified multiobjective artificial bee colony algorithm. The existing single-objective optimization methods fail to solve the concerned problem. This work can find the optimal routes of the repositioning vehicles along with the number of desired parked bikes of corresponding stations. The experimental results indicate that the proposed method is highly effective and can greatly and readily help decision-makers better manage the BSS of a practical size.

With growing environmental and sustainability-related concerns, recovery optimization of mechanical products has been gaining increased exposure. It facilitates environmental sustainability through the improvement in the life-cycle material efficiency and reduction in environmental impact with disassembly sequence planning, component reuse, and material recycling. Traditional product recovery separates end-of-life (EOL) products into components and selects EOL options of components. However, there are many practical cases in which the recovery of a set of subassemblies and components leads to better net revenue than that of a complete set of single components. This paper proposes to model and optimize hybrid disassembly and EOL operations of product recovery to maximize the recovery profit and minimize the environmental impact. Flexible process planning of hybrid disassembly determines a disassembly level by identifying the reusability of subassemblies and disassembly sequences mixed with subassemblies and components. Optimal EOL decisions for each subassembly and component are investigated such that the economic and environmental objectives can be achieved. Finally, a case study is described to illustrate the proposed method and the influence on decision variables of the tradeoff between the recovery profit and environmental impact is discussed. Note to Practitioners-This paper deals with the process planning and EOL decision-making problem of product recovery. Based on hybrid disassembly, this paper proposes a flexible process planning and EOL decision-making method for product recovery. Flexible process planning of hybrid disassembly determines a disassembly level by identifying the reusability of subassemblies and disassembly sequences mixed with subassemblies and components. Optimal EOL decisions for each subassembly and component are investigated such that the economic and environmental objectives can be achieved. The goal of this paper is to model and optimize hybrid disassembly and EOL operations of product recovery to maximize the recovery profit and minimize the environmental impact. The results demonstrate that the proposed method can leads to small environmental impact and low cost. Subassemblies and components with high reliability and expensive price are suggested to be destined for reuse. Minimizing transportation distances is more effective to reduce product recovery cost. Such results can help decision makers to perform better judgments when a disassembly process of an EOL product is executed.

Disassembly sequencing is important for remanufacturing and recycling used or discarded products. AND/OR graphs (AOGs) have been applied to describe practical disassembly problems by using “AND” and “OR” nodes. An AOG-based disassembly sequence planning problem is an NP-hard combinatorial optimization problem. Heuristic evolution methods can be adopted to handle it. While precedence and “AND” relationship issues can be addressed, OR (exclusive OR) relations are not well addressed by the existing heuristic methods. Thus, an ineffective result may be obtained in practice. A conflict matrix is introduced to cope with the exclusive OR relation in an AOG graph. By using it together with precedence and succession matrices in the existing work, this work proposes an effective triple-phase adjustment method to produce feasible disassembly sequences based on an AOG graph. Energy consumption is adopted to evaluate the disassembly efficiency. Its use with the traditional economical criterion leads to a novel dual-objective optimization model such that disassembly profit is maximized and disassembly energy consumption is minimized. An improved artificial bee colony algorithm is developed to effectively generate a set of Pareto solutions for this dual-objective disassembly optimization problem. This methodology is employed to practical disassembly processes of two products to verify its feasibility and effectiveness. The results show that it is capable of rapidly generating satisfactory Pareto results and outperforms a well-known genetic algorithm.

Disassembly planning aims to perform the optimal disassembly sequence given a used or obsolete product in terms of cost and environmental impact. This paper presents a new multiobjective programming model for the target disassembly sequencing. It proposes an improved multiobjective ant colony algorithm to derive optimal target disassembly sequences. This work also establishes some indices on disassembly scheme evaluation and a fuzzy integral method to evaluate the obtained disassembly scheme. A CNC machine tool example is given to illustrate the proposed models and the effectiveness of the proposed algorithm. Both theoretical and simulation results demonstrate that the proposed approach can perform the quantitative analysis of a disassembly process effectively. Such results can help decision makers select the best plans and sequences when executing a disassembly process of a product.

With the development of communication and control technology, intelligent transportation systems (ITS) have received increasing attention from both industry and academia. However, plenty of studies providing different formulations for ITS depend on Master Control Center and require a high level of hardware configuration. The systematized technologies for distributed architectures are still not explored in detail. In this paper, we proposed a novel distributed cyber–physical system for connected and automated vehicles, and related methodologies are illustrated. Every vehicle in this system is modeled as a double-integrator and supposed to travel along a desired trajectory for maintaining a rigid formation geometry. The desired trajectory is generated by reference leading vehicles using information from multiple sources, while ordinary following vehicles use velocity and position information from their nearest neighbors and sensor information from on-board sensors to correct their own performance. Information graphs are used to illustrate the interaction topology between connected and automated vehicles. Edge computing technology is used to analyze and process information, such that the risk of privacy leaks can be greatly reduced. The performance scaling laws for the network with a one-dimensional information graph are generalized to networks withD-dimensional information graphs, and the results of the experiments show that the performance of the connected and automated vehicles matches very well with analytic predictions. Some design guidelines and open questions are provided for the future study.

A new force is introduced in the social force model (SFM) for computing following behavior in pedestrian counterflow, whereby an individual tries to approach others in the same direction to avoid conflicts with pedestrians from the opposite direction. The force, like a kind of gravitation, is modeled based on the movement state and visual field of the pedestrian, and is added to the classical SFM. The modified model is presented to study the impact of following behavior on the process of lane formation, the conflict, the number of lanes formed, and the traffic efficiency in the simulations. Simulation results show that the following behavior has a significant effect on the phenomenon of lane formation and the traffic efficiency.

With the significant improvements in the Energy Internet, we have witnessed the explosion of multisource energy big data, whose characteristics of vast volume, fast velocity, and diverse variety not only formulate an essential infrastructure of the Energy Internet, but also bring threats to the system's stability. In this paper, we concern with the system-level stability issues in the Energy Internet and study how to maintain a stable and healthy energy network environment. To this end, we propose a system-level stability evaluation model in the Energy Internet based on a critical energy function to explore small disturbance stability region (SDSR), where SDSR can be acquired via estimating the operational data threshold of distributed generations. The threshold is estimated based on energy consumption rather than equilibrium nodes, which applies the energy function theory and reduces the computation complexity. Moreover, in our proposed model, we add the big data approximate analytics algorithm into hyperplane fitting to optimize and analyze the SDSR. Simulation results on SDSR in a single dominant oscillation mode and multiple dominant oscillation mode have demonstrated the advantages and superiority of our proposed method over the prior schemes.

The aim of this paper is to develop a fuzzy group programming decision-making method to effectively assess manufacturing cloud. Evidential reasoning approach is applied to aggregate group fuzzy information source provided by organizing group of manufacturing cloud and an agreement result of collaborative decision then can be concluded. Multiple constraints and multiple objective optimization models are constructed for maximization of consumer satisfaction degree, minimization of manufacturing cost and manufacturing service implementation difficulty. Strength pareto evolutionary algorithm is adopted to acquire pareto solutions set. Fuzzy optimal selection method is employed to choose the most feasible solution from pareto solutions set. Finally, a living example related to the manufacturing work of air separation equipment is given to validate the approach we proposed.