Computers & Industrial Engineering最新文献_第8页

Risk propagation and intervention in a complex supply chain network under public emergency 突发公共事件下复杂供应链网络的风险传播与干预

IF 6.5 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Industrial Engineering

Pub Date : 2026-01-03 DOI: 10.1016/j.cie.2025.111797

Lin Jiang , Jingxin Liang , Yafei Yi , Dongmei Liu

Different from previous studies, this paper is motivated to examine supply chain risk from a complex network perspective rather than a simple supply chain structure, and considers risk propagation and intervention in a complex supply chain network (CSCN) under public emergency. Facing the crisis triggered by public emergency, the government decides to aid partial enterprises in order to improve the stability of the CSCN. Inspired by the similarity between the risk propagation in the CSCN and the diffusion of infectious disease in the social network, this study tries to develop a new risk propagation model of the CSCN based on the SEIR system dynamics theory in infectious disease field. Using the classic basic reproduction number (BRN) concept, which means the average number of enterprises that one infected enterprise will infect, this study examines the risk propagation behavior and its threshold in the CSCN. This study finds out that the government can effectively control the risk propagation in the CSCN by way of controlling the BRN. Under the control of government, if the BRN is smaller than one, then the risk will withdraw. On the contrary, if the BRN is bigger than one, then the risk will diffuse to the entire supply chain network at all. This study also finds out that the risk propagation in the CSCN is relatively sensitive to the recovery probability. This study further conducts simulation analysis of risk propagation, the results demonstrate that the risk will be controlled or abated by controlling the BRN.

与以往研究不同的是，本文从复杂网络的角度而非简单的供应链结构来考察供应链风险，并考虑突发公共事件下复杂供应链网络（CSCN）的风险传播与干预。面对突发公共事件引发的危机，政府决定对部分企业进行援助，以提高CSCN的稳定性。鉴于CSCN中的风险传播与传染病在社会网络中的传播具有相似性，本研究试图基于传染病领域的SEIR系统动力学理论，建立CSCN的风险传播新模型。本文采用经典的基本复制数（BRN）概念，即一个受感染企业将感染的企业平均数量，研究了CSCN中的风险传播行为及其阈值。本研究发现，政府可以通过控制BRN来有效控制CSCN中的风险传播。在政府的控制下，如果BRN小于1，那么风险就会消失。相反，如果BRN大于1，则风险将完全扩散到整个供应链网络。研究还发现，CSCN中的风险传播对恢复概率相对敏感。本研究进一步对风险传播进行了仿真分析，结果表明通过控制BRN可以控制或减弱风险。

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引用次数: 0

A stochastic bilevel model to manage symbiotic flows in an Industrial Symbiosis network under demand uncertainty 需求不确定性下工业共生网络中共生流动的随机双层模型

IF 6.5 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Industrial Engineering

Pub Date : 2026-01-03 DOI: 10.1016/j.cie.2026.111800

Gülesin Sena Daş , Murat Yeşilkaya , Büşra Altınkaynak , Burak Birgören

Industrial symbiosis (IS) networks offer a valuable means of reducing environmental impacts by optimizing by-products utilization and minimizing resource consumption. However, managing these networks remains challenging due to uncertainties in supply and demand, as well as the hierarchical decision-making structures within eco-industrial parks (EIPs). This study presents a novel stochastic bilevel programming model to coordinate by-product flows in an IS network. The model balances the EIP authority’s objective of minimizing raw material use with companies’ objectives of maximizing profits under uncertain demand. Unlike single-objective models, our bilevel approach uncovers intricate relationships between conflicting objectives. Applied to a theoretical IS network in the forest products industry, the model shows that demand fluctuations disrupt material flows and increase raw material consumption, worsening the EIP authority’s environmental objectives, however, paradoxically, boost company profits by encouraging higher production. This interesting trade-off between the hierarchical objectives along with the model’s ability to maintain feasible operations under varying demand conditions demonstrates the network’s resilience.

工业共生（IS）网络通过优化副产品利用和最大限度地减少资源消耗，提供了减少环境影响的宝贵手段。然而，由于供需的不确定性，以及生态工业园区（eip）内部的分层决策结构，管理这些网络仍然具有挑战性。提出了一种新的随机双层规划模型来协调IS网络中的副产物流。该模型平衡了EIP当局在需求不确定情况下原材料使用最小化的目标和企业利润最大化的目标。与单目标模型不同，我们的双层方法揭示了冲突目标之间复杂的关系。将该模型应用于林产品行业的理论信息系统网络，结果表明，需求波动扰乱了物质流动，增加了原材料消耗，使EIP当局的环境目标恶化，然而，矛盾的是，通过鼓励更高的产量，提高了公司的利润。分层目标与模型在不同需求条件下维持可行操作的能力之间的这种有趣的权衡表明了网络的弹性。

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引用次数: 0

Optimization of ambulance services sequencing and scheduling daily decisions with minimizing delay 优化救护车服务排序和调度日常决策，尽量减少延误

IF 6.5 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Industrial Engineering

Pub Date : 2026-01-02 DOI: 10.1016/j.cie.2026.111803

Mohammad Javad Eslami , Mohsen Varmazyar

Ambulances, one of the essential resources in the emergency medical service (EMS), are crucial in transporting patients to hospitals and saving lives. This research addresses the ambulance service scheduling problem (ASSP) for daily planning decisions by minimizing total weighted tardiness. A mixed integer linear mathematical model for the research problem is developed. Since the research problem is shown to be NP-hard, two population-based genetic algorithm (GA) and particle swarm optimization (PSO), and two solution-based, simulated annealing (SA) and tabu search (TS) meta-heuristics are proposed to solve this problem. In addition, the Lagrangian relaxation (LR) and Benders decomposition methods are employed to find effective lower bounds. Random test problems with small, medium, and large sizes are generated and solved by the proposed algorithms to evaluate their performance. Numerical results show that the LR and Benders decomposition can find efficient lower bounds with approximately 4 % and 6 % gap rates, respectively. Furthermore, ANOVA and Tukey’s HSD tests indicate that the GA, PSO, and SA algorithms perform better in small-, medium-, and large-size problems, respectively. It is noticeable that the best-obtained meta-heuristic solutions have a gap rate of approximately 6.21 %, with the best-obtained lower bounds. Moreover, due to the ASSP problem’s stochastic nature, we develop a two-stage stochastic programming model by considering each mission’s weight and time under uncertainty. Additionally, considering enough scenarios, which in our research is 40, the optimal value can be closely approximated. The outputs of this research are employed for a real-world case study as well. Finally, some managerial and practical insights are discussed based on the results.

救护车是紧急医疗服务的重要资源之一，在将病人送往医院和挽救生命方面起着至关重要的作用。本研究通过最小化总加权延迟来解决救护车服务调度问题（ASSP）的日常规划决策。建立了研究问题的混合整数线性数学模型。针对研究问题具有np困难的特点，提出了两种基于种群的遗传算法（GA）和粒子群算法（PSO），以及两种基于解的模拟退火算法（SA）和禁忌搜索（TS）元启发式算法来解决该问题。此外，采用拉格朗日松弛（LR）和Benders分解方法寻找有效下界。通过生成和解决小、中、大尺寸的随机测试问题来评估算法的性能。数值结果表明，LR分解和Benders分解分别可以找到间隙率约为4%和6%的有效下界。此外，方差分析和Tukey的HSD检验表明，GA、PSO和SA算法分别在小型、中型和大型问题中表现更好。值得注意的是，最佳启发式解的间隙率约为6.21%，并具有最佳下界。此外，由于ASSP问题的随机性，我们建立了一个考虑不确定条件下每个任务的权值和时间的两阶段随机规划模型。此外，考虑到足够多的场景，在我们的研究中是40，最优值可以很接近。本研究的产出也用于现实世界的案例研究。最后，根据研究结果讨论了一些管理和实践见解。

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引用次数: 0

Hierarchical spatio-temporal dynamic dependency learning for multivariate operating status prediction of pump station units 基于分层时空动态依赖学习的泵站机组多变量运行状态预测

IF 6.5 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Industrial Engineering

Pub Date : 2026-01-02 DOI: 10.1016/j.cie.2025.111796

Panpan Qiu, Jianzhuo Yan, Yongchuan Yu, Hongxia Xu

In complex industrial systems such as those in the energy, transportation, and water conservancy sectors,accurate prediction of the operating status of key equipment is essential for ensuring system safety and continuous operation. Taking a pump station unit as an example, their operational data typically exhibit multivariate coupling, noise interference, and complex spatiotemporal dynamics, which collectively pose significant challenges to prediction accuracy. To tackle these issues, we propose HSTD-Net, a Hierarchical and Dynamic Spatiotemporal Dependency Network for multivariate time series forecasting. HSTD-Net first introduces a patch division mechanism to enhance local feature representations; then (i) employs dynamic convolution to adaptively capture short-term intra-patch dynamics; (ii) leverages self-attention to model long-term inter-patch dependencies; and (iii) integrates a Centralized Aggregation–Redistribution (CAR) module to strengthen global cross-variable interactions. By explicitly modeling these hierarchical and dynamic dependencies, HSTD-Net achieves more accurate and robust forecasting. Extensive experiments on operational data from a real-world pump station demonstrate that HSTD-Net consistently outperforms existing state-of-the-art baseline methods across multiple evaluation metrics, highlighting its effectiveness and practical value in industrial scenarios.

在能源、交通、水利等复杂的工业系统中，对关键设备的运行状态进行准确预测是保证系统安全、持续运行的关键。以泵站为例，其运行数据通常表现为多元耦合、噪声干扰和复杂的时空动态，这些都对预测精度构成了重大挑战。为了解决这些问题，我们提出了HSTD-Net，一个用于多变量时间序列预测的分层和动态时空依赖网络。HSTD-Net首先引入了补丁分割机制来增强局部特征表示；然后(i)采用动态卷积自适应捕捉斑块内的短期动态；（ii）利用自我关注来模拟长期补丁间依赖关系；（iii）集成了集中式聚集-再分配（CAR）模块，以加强全球跨变量交互。通过显式地对这些分层和动态依赖关系进行建模，HSTD-Net实现了更准确和更健壮的预测。对实际泵站运行数据的大量实验表明，HSTD-Net在多个评估指标上始终优于现有的最先进的基线方法，突出了其在工业场景中的有效性和实用价值。

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引用次数: 0

A fuzzy-integrated hybrid gradient-boosted AI system for resilient, safe, and sustainable heritage tourism planning 一个用于弹性、安全和可持续遗产旅游规划的模糊集成混合梯度增强人工智能系统

IF 6.5 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Industrial Engineering

Pub Date : 2026-01-02 DOI: 10.1016/j.cie.2025.111798

Sarayut Gonwirat , Rapeepan Pitakaso , Surajet Khonjun , Thanatkij Srichok , Chutchai Kaewta , Chawis Boonmee , Chompoonoot Kasemset , Peerawat Luesak , Ganokgarn Jirasirilerd

Heritage tourism trip planning is essential for ensuring visitor satisfaction, preserving cultural sites, and supporting local economies, especially in regions like northern Thailand. Existing methodologies often fail to comprehensively address sustainability, safety, resilience, economic impact, and cultural preservation. To address this gap, this research presents the Hybrid Gradient-Boosted Artificial Intelligence System (GBAMIS). GBAMIS employs a mixed approach for initial solution construction and uses Gradient Boosted Machines (GBM) to optimize performance within the Artificial Multiple Intelligence System (AMIS) framework. Additionally, 12 novel heuristic-based improvement equations are introduced. Data from 125 attractions and feedback from 450 tourists in northern Thailand were used to validate the system. Key indices, including Sustainability, Safety, Resilience, Economic Impact, and Cultural Preservation, were assessed. GBAMIS outperformed existing methods, with hypervolume improvements of 18.75%, 14.46%, and 6.74% over AMIS, GRASP-VND, and MRL-AMIS, respectively. It also showed significant gains in Pareto optimal solutions, improving by 19.48%, 15%, and 6.98%. Furthermore, GBAMIS demonstrated strong performance under uncertainty through fuzzy membership management, handling varying values with consistent results. This adaptability makes it suitable for dynamically changing environments, offering a novel solution for heritage tourism planning. The findings highlight GBAMIS’s potential to enhance cultural site preservation, visitor experiences, and economic support for local communities, contributing to advancements in intelligent tourism management systems.

遗产旅游行程规划对于确保游客满意度、保护文化遗址和支持当地经济至关重要，尤其是在泰国北部等地区。现有的方法往往不能全面解决可持续性、安全性、复原力、经济影响和文化保护问题。为了解决这一差距，本研究提出了混合梯度增强人工智能系统（GBAMIS）。GBAMIS采用混合方法进行初始解决方案构建，并使用梯度增强机（GBM）在人工多智能系统（AMIS）框架内优化性能。此外，还提出了12个新的启发式改进方程。来自泰国北部125个景点的数据和450名游客的反馈用于验证该系统。主要指标包括可持续性、安全性、复原力、经济影响和文化保护。GBAMIS优于现有的方法，比AMIS、GRASP-VND和MRL-AMIS分别提高了18.75%、14.46%和6.74%的超大容量。在Pareto最优解上也有显著的提高，分别提高了19.48%、15%和6.98%。此外，通过模糊隶属度管理，GBAMIS在不确定情况下表现出较强的性能，处理不同的值，结果一致。这种适应性使其适应动态变化的环境，为遗产旅游规划提供了一种新的解决方案。研究结果强调了GBAMIS在加强文化遗址保护、游客体验和为当地社区提供经济支持方面的潜力，有助于智能旅游管理系统的发展。

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引用次数: 0

Competition and cooperation evaluation for multi-modal railway network: A multi-leader–follower approach 多式联运铁路网络的竞争与合作评价：多领导-跟随者方法

IF 6.5 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Industrial Engineering

Pub Date : 2025-12-31 DOI: 10.1016/j.cie.2025.111759

Fangsheng Wang , Pengling Wang , Hanchuan Pan , Yuanchun Huang , Nikola Bešinović , Andrea D’Ariano

The multi-modal railway network, comprising high-speed rail (HSR), intercity rail (ICR), suburban rail (SUR), and urban rail transit (URT), has been gaining increasing attention due to its reliability and social benefits. These different rail transit modes often cooperate to provide seamless multi-modal travel services for long-distance passengers, while simultaneously facing competition due to overlapping passenger demand. However, most existing studies on ticket pricing and train scheduling do not fully account for the competition–cooperation relationships among these four rail transit modes. To address this gap, this study presents a multi-leader–follower game model that integrates ticket pricing and train scheduling (including line planning and timetabling) while considering the competitive and cooperative interactions within the multi-modal railway network. The model incorporates a simulation-based passenger assignment approach at the lower level and a decision-making framework at the upper level, aiming to approximate a Nash equilibrium solution among the various railway operators. The bus system is introduced only to provide a realistic competitive background, preventing a purely railway-monopolized setting and allowing us to better analyze the cooperative–competitive strategies among the four railway systems. An improved Nash Q-learning algorithm is developed to iteratively determine the approximated Nash equilibrium solution for the proposed multi-leader–follower game model. The effectiveness of the proposed method is demonstrated in a case study based on the multi-modal railway network in Jiangsu Province and Shanghai, China. Our results show that the proposed method can effectively optimize both ticket pricing and train scheduling in the multi-modal railway network under various competition–cooperation scenarios. A viable cooperation strategy involves encouraging passengers with short-distance trips to use urban transport modes (such as SUR and URT) while reserving more available seats on intercity transport modes (like HSR and ICR) for long-distance passengers. This strategy helps optimize the overall efficiency of the multi-modal transportation system.

由高速铁路（HSR）、城际铁路（ICR）、城郊铁路（SUR）和城市轨道交通（URT）组成的多式联运铁路网络因其可靠性和社会效益而越来越受到关注。这些不同的轨道交通方式往往相互合作，为长途旅客提供无缝的多式联运服务，同时由于旅客需求重叠而面临竞争。然而，现有的大多数关于票价和列车调度的研究都没有充分考虑到这四种轨道交通方式之间的竞争合作关系。为了解决这一差距，本研究提出了一个多领导-追随者博弈模型，该模型将车票定价和列车调度（包括线路规划和调度）集成在一起，同时考虑了多式联运铁路网络中的竞争和合作互动。该模型结合了基于仿真的下层乘客分配方法和上层决策框架，旨在近似各铁路运营商之间的纳什均衡解决方案。公交系统的引入只是为了提供一个现实的竞争背景，避免了纯粹的铁路垄断设置，使我们能够更好地分析四个铁路系统之间的合作竞争策略。提出了一种改进的纳什q -学习算法，用于迭代确定多领导-追随者博弈模型的近似纳什均衡解。以江苏省和上海市的多式联运铁路网络为例，验证了该方法的有效性。研究结果表明，该方法可以有效地优化多模式铁路网络中各种竞争-合作场景下的票价和列车调度。一个可行的合作策略是鼓励短途出行的乘客使用城市交通方式（如高铁和城际轨道交通），同时为长途乘客保留更多城际交通方式（如高铁和ICR）的可用座位。这一策略有助于优化多式联运系统的整体效率。

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引用次数: 0

Benchmarking state-of-the-art large-scale global optimization algorithms for tool path planning in five-axis flank milling: A comparative performance analysis 五轴侧铣削刀具轨迹规划的基准-最先进的大规模全局优化算法：比较性能分析

IF 6.5 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Industrial Engineering

Pub Date : 2025-12-30 DOI: 10.1016/j.cie.2025.111780

Jin Yi , Hanhai Shou , Xinyu Li , Wuzhuang Zhou , Chih-Hsing Chu

Five-axis flank milling is an advanced machining technology widely employed for producing complex geometries, where tool path planning directly influences surface quality and machining efficiency. However, the mathematical nature of the problem presents a large-scale global optimization challenge, involving the search for optimal solutions within a high-dimensional decision space. To address this issue, this paper provides a review of eight state-of-the-art large-scale global optimization algorithms and conducts a comparative evaluation of their performance on representative test geometries. Analyses of the test results reveal key factors that determine the computational performance of the algorithms from multiple perspectives. These insights further inform the design of advanced evolutionary optimization algorithms for effective tool path planning in five-axis flank milling.

五轴侧铣削加工是一种广泛应用于复杂几何形状加工的先进加工技术，其刀具轨迹规划直接影响表面质量和加工效率。然而，该问题的数学性质提出了一个大规模的全局优化挑战，涉及在高维决策空间中寻找最优解。为了解决这一问题，本文综述了八种最先进的大规模全局优化算法，并对其在代表性测试几何上的性能进行了比较评估。对测试结果的分析从多个角度揭示了决定算法计算性能的关键因素。这些见解进一步为五轴侧铣削中有效刀具轨迹规划的先进进化优化算法的设计提供了信息。

引用次数: 0

The role of voluntary carbon offsetting in advancing firm’s carbon neutrality and pricing strategies in the presence of eco-conscious consumers 在有生态意识的消费者在场的情况下，自愿碳抵消在推进企业碳中和和定价策略中的作用

IF 6.5 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Industrial Engineering

Pub Date : 2025-12-30 DOI: 10.1016/j.cie.2025.111762

Guangyu Wan , Xie Jiang , Jingfen Hua , Yu Cao , Dong Li

The pursuit of carbon neutrality is becoming increasingly vital for companies striving to align with global decarbonization goals and meet the growing demand for sustainable products. This study examines the integration of voluntary carbon offsets into corporate decarbonization strategies within a heterogeneous consumer market. Using a game-theoretic approach, we investigate how profit-driven firms balance production-related emission reduction efforts, voluntary carbon offset purchases, and pricing strategies. Our analysis reveals three key insights. First, allowing firms to utilize voluntary carbon offsets provides a cost-efficient alternative to direct emission reductions, enabling full carbon neutrality while meeting consumer expectations. Second, permitting consumers to purchase offsets shifts some responsibility for emissions from firms to eco-conscious customers. This reduces firms’ incentives to prioritize direct emission reductions but enhances overall societal environmental performance by lowering net emissions. Third, incorporating voluntary offsetting enables price differentiation across consumer segments, increasing profitability while promoting environmental sustainability. These findings underscore the strategic importance of integrating voluntary carbon offsetting into corporate production and pricing decisions. They provide actionable insights for firms navigating the transition to carbon-neutral operations, balancing economic and environmental goals.

对于努力与全球脱碳目标保持一致并满足对可持续产品日益增长的需求的公司来说，追求碳中和正变得越来越重要。本研究考察了在异质消费市场中自愿碳补偿与企业脱碳战略的整合。运用博弈论的方法，我们研究了利润驱动型企业如何平衡与生产相关的减排努力、自愿碳抵消购买和定价策略。我们的分析揭示了三个关键见解。首先，允许企业利用自愿碳抵消为直接减排提供了一种具有成本效益的替代方案，在满足消费者期望的同时实现了完全的碳中和。其次，允许消费者购买碳补偿将一些排放责任从企业转移到有环保意识的消费者身上。这降低了企业优先考虑直接减排的动机，但通过降低净排放提高了整体社会环境绩效。第三，纳入自愿补偿可以实现不同消费群体之间的价格差异化，在提高盈利能力的同时促进环境的可持续性。这些发现强调了将自愿碳抵消纳入企业生产和定价决策的战略重要性。它们为企业向碳中和运营转型、平衡经济和环境目标提供了可行的见解。

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引用次数: 0

Virtual-physical mapping network-driven digital twin for intelligent fault diagnosis with imbalanced samples 基于虚拟物理映射网络的不平衡样本智能故障诊断数字孪生

IF 6.5 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Industrial Engineering

Pub Date : 2025-12-28 DOI: 10.1016/j.cie.2025.111783

Quanning Xu , Guangrui Wen , Zihao Lei , Qing Ni , Ke Feng , Rui Qin , Jing Huang , Xuefeng Chen

In advanced manufacturing systems, intelligent fault diagnosis of rolling bearings serves as a pivotal technology for ensuring equipment reliability and improving operational efficiency. Nevertheless, acquiring high-quality monitoring data across multiple fault states in real industrial environments remains a major challenge. This limitation makes achieving high-accuracy diagnosis under imbalanced samples particularly difficult. Digital twin technology provides a promising paradigm to alleviate these constraints. This research introduces a digital twin-based intelligent diagnosis framework under imbalanced samples. First, a nonlinear 6-DOF dynamic model is formulated for digital twin construction, generating high-fidelity simulated vibration responses. Second, a novel attention-assisted operational CycleGAN architecture is developed for cross-domain information exchange between virtual and physical domains. This mapping network integrates a generator constructed using an enhanced feature pyramid network and a discriminator based on the PatchGAN architecture. By leveraging self-organized operational neural network and attention-enhanced skip connections, the network strengthens intra-layer feature extraction and inter-layer feature transmission, thereby facilitating effective information exchange between virtual and physical entities. Finally, experimental validation demonstrates that the Pearson correlation coefficient between the normalized twin and real signal exceeds 0.9. The twin signal enables deployment of diagnostic models, achieving reliable fault diagnosis in data scarcity scenarios.

在先进制造系统中，滚动轴承故障智能诊断是保证设备可靠性和提高运行效率的关键技术。然而，在真实工业环境中获取跨多种故障状态的高质量监测数据仍然是一个主要挑战。这一限制使得在不平衡样本下实现高精度诊断特别困难。数字孪生技术为缓解这些限制提供了一个有前途的范例。介绍了一种基于数字孪生的不平衡样本智能诊断框架。首先，建立了数字双体结构的非线性六自由度动力学模型，生成了高保真的仿真振动响应。其次，开发了一种新颖的注意力辅助操作CycleGAN架构，用于虚拟和物理域之间的跨域信息交换。该映射网络集成了使用增强特征金字塔网络构建的生成器和基于PatchGAN架构的鉴别器。该网络利用自组织的操作神经网络和注意力增强的跳跃连接，加强了层内特征提取和层间特征传输，从而促进了虚拟实体和物理实体之间有效的信息交换。实验验证表明，归一化后的孪生信号与真实信号的Pearson相关系数超过0.9。双信号支持诊断模型的部署，在数据稀缺场景下实现可靠的故障诊断。

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引用次数: 0

A comprehensive review of time between events control charts: models, applications, and future directions 事件之间的时间控制图的全面审查：模型，应用程序和未来的方向

IF 6.5 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Industrial Engineering

Pub Date : 2025-12-27 DOI: 10.1016/j.cie.2025.111789

Mohammadreza Mirzaei Novin , Amirhossein Amiri , Philippe Castagliola

The increasing demand for high-quality processes has driven growing interest in control charts designed for monitoring rare events. Among these, Time Between Events control charts have emerged as powerful tools, yet a comprehensive literature review dedicated to this field has been lacking until now. This gap has limited the ability of researchers and practitioners to identify key methodological advances, unresolved challenges, and emerging research directions. To address this need, the present study provides the first extensive review of TBE control charts, covering 113 studies published between 2000 and 2025. A multi-dimensional classification framework is introduced, which organizes the literature according to distributional assumptions, monitoring techniques, performance metrics, monitoring phases, event polarity (positive vs. negative events), and data structures (univariate, multivariate, and combined TBE with amplitude). The review further analyzes publication outlets and presents comprehensive reference tables to support quick identification of relevant methods. Finally, the paper highlights critical research gaps—including limited work on positive events, nonparametric multivariate monitoring, adaptive and hybrid methods, and machine learning integration—and proposes a forward-looking agenda to advance statistical process monitoring in increasingly complex environments.

对高质量过程的日益增长的需求推动了对为监视罕见事件而设计的控制图的日益增长的兴趣。其中，事件间隔时间控制图已经成为一种强大的工具，但到目前为止，还缺乏专门针对该领域的全面文献综述。这一差距限制了研究人员和实践者识别关键方法进展、未解决的挑战和新兴研究方向的能力。为了满足这一需求，本研究首次对TBE控制图进行了广泛的回顾，涵盖了2000年至2025年间发表的113项研究。介绍了一个多维分类框架，该框架根据分布假设、监测技术、性能指标、监测阶段、事件极性（积极与消极事件）和数据结构（单变量、多变量和结合TBE与振幅）组织文献。本文进一步分析了出版渠道，并提供了全面的参考表格，以支持快速识别相关方法。最后，本文强调了关键的研究差距——包括在积极事件、非参数多元监测、自适应和混合方法以及机器学习集成方面的有限工作——并提出了一个前瞻性议程，以促进在日益复杂的环境中进行统计过程监测。

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引用次数: 0