Advanced Engineering Informatics最新文献_第3页

A nonlinear dynamics method using multi-sensor signal fusion for fault diagnosis of rotating machinery

IF 8 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2025-02-22 DOI: 10.1016/j.aei.2025.103190

Fei Chen , Zhigao Zhao , Xiaoxi Hu , Dong Liu , Xiuxing Yin , Jiandong Yang

Deep mining abnormal information from operation data is a crucial step in fault diagnosis of equipment, and it holds significant importance for ensuring the efficient operation of rotating machinery. The nonlinear dynamics methods represented by multivariate multiscale entropy have shown good application effects in quantifying the fault characteristics of rotating machinery using multiple sensor signals. However, these methods essentially belong to the category of data-level fusion, which suffers from drawbacks such as poor real-time performance, limited capability to handle only similar types of sensors, and significant influence from sensor information. This paper develops a novel tool named enhanced hierarchical Poincaré plot index (EHPPI), for extracting anomaly information from multi-source signals via feature-level fusion. Firstly, the Poincaré plot index is extended to create the EHPPI, allowing for the extraction of information from signals at various frequency scales. Subsequently, EHHPI is utilized to extract information from all channel signals. Ultimately, we concatenate the information extracted from all channels by EHPPI to form features and integrate them with random forests to identify faults in rotating machinery. The EHPPI and other popular nonlinear dynamics metrics are applied in different scenarios, such as simulation faults, experimental bench faults, and real machine faults, whose results strongly prove its advantages. The EHPPI has a favorable effect on improving the operational efficiency of rotating machinery.

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引用次数: 0

Developing A novel AI enabled extended reality system for real-time automatic facial expression recognition and system performance evaluation

IF 8 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2025-02-22 DOI: 10.1016/j.aei.2025.103207

Amirarash Kashef , Yu Wang , Mohammad Nafe Assafi , Junfeng Ma , Jun Wang , J. Adam Jones , Ladda Thiamwong

Facial Expression Recognition (FER) is vital for understanding human behavior but faces challenges from varying facial features due to different poses, lighting, and angles. Addressing the growing demand for real-time FER is critical. Extended Reality (XR) offers significant potential in training, education, healthcare, user experience, and relevant data collection. This study aims to develop an AI-enabled XR system for FER by combining a novel Depthwise Separable Convolutional Neural Network (DS-CNN) approach with XR technology. The FER2013 image dataset was used to train and build the proposed FER model. The model’s performance was validated using two separate image datasets, demonstrating that the proposed CNN model outperformed existing models on both. Subsequently, the CNN model was integrated with Microsoft HoloLens 2 XR technology to create a real-time, automatic FER system. System evaluation was conducted using System Usability Scale (SUS) and NASA-TLX measures, with results indicating that the proposed smart system is high usability and lower cognitive workload compared with FER using eyes. The AI-enabled XR system offers significant practical applications and potential across various domains, providing valuable managerial insights. The integration of CNN with XR technology represents a substantial advancement in real-time FER, offering improved accuracy and usability under diverse conditions.

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引用次数: 0

Deep learning-based rebar detection and instance segmentation in images

IF 8 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2025-02-22 DOI: 10.1016/j.aei.2025.103224

Tao Sun , Qipei Fan , Yi Shao

Automated rebar cage assembly and quality inspection rely on reliable rebar perception. Recent studies have explored image-based rebar perception via object detection and instance segmentation algorithms. However, existing models are limited across various scenarios, especially with different rebar categories, arrangement patterns, and camera views, which limits their application. This is primarily attributed to the absence of a benchmark considering these factors. This study introduces an image benchmark designed for the effective training and selection of rebar detection and instance segmentation algorithms. It is the first to encompass two types of commonly used rebars, multiple camera views, and various rebar placement patterns at different assembly phases in a single dataset. Six object detection methods and four instance segmentation methods are evaluated to assess the applicability of the state-of-the-art methods. Additionally, a new shape-prior-based post-processing method is developed to address the merged detection problem in clustering. The experiment shows that Deformable DETR and Mask2Former achieved the highest bounding box mAP (80.4) and mask mAP (66.3) respectively. The Simple Copy-Paste technique was introduced, improving the mask mAP of Mask2Former by 2.8 points. Finally, the developed model was validated in the real-world scenarios of three downstream tasks. Notably, in the rebar spacing measurement task, the proposed post-processing method improves Mask2Former by increasing its bounding box mAP by 18.0 and mask mAP by 2.4.

钢筋笼的自动装配和质量检测依赖于可靠的钢筋感知。最近的研究通过对象检测和实例分割算法探索了基于图像的钢筋感知。然而，现有的模型在各种场景下都存在局限性，尤其是在不同的钢筋类别、排列模式和相机视图下，这限制了它们的应用。这主要是因为缺乏考虑这些因素的基准。本研究介绍了一种图像基准，旨在有效地训练和选择钢筋检测和实例分割算法。这是首个在单一数据集中包含两种常用钢筋、多个摄像机视图以及不同装配阶段的各种钢筋放置模式的基准。对六种对象检测方法和四种实例分割方法进行了评估，以评估最先进方法的适用性。此外，还开发了一种新的基于形状优先的后处理方法，以解决聚类中的合并检测问题。实验结果表明，Deformable DETR 和 Mask2Former 分别获得了最高的边界框 mAP（80.4）和掩膜 mAP（66.3）。引入简单复制粘贴技术后，Mask2Former 的掩膜 mAP 提高了 2.8 个点。最后，在三个下游任务的实际场景中对所开发的模型进行了验证。值得注意的是，在钢筋间距测量任务中，所提出的后处理方法改善了 Mask2Former，使其边界框 mAP 增加了 18.0，掩膜 mAP 增加了 2.4。

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引用次数: 0

Empowering LLMs by hybrid retrieval-augmented generation for domain-centric Q&A in smart manufacturing

IF 8 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2025-02-22 DOI: 10.1016/j.aei.2025.103212

Yuwei Wan , Zheyuan Chen , Ying Liu , Chong Chen , Michael Packianather

Large language models (LLMs) have shown remarkable performances in generic question-answering (QA) but often suffer from domain gaps and outdated knowledge in smart manufacturing (SM). Retrieval-augmented generation (RAG) based on LLMs has emerged as a potential approach by incorporating an external knowledge base. However, conventional vector-based RAG delivers rapid responses but often returns contextually vague results, while knowledge graph (KG)-based methods offer structured relational reasoning at the expense of scalability and efficiency. To address these challenges, a hybrid KG-Vector RAG framework that systematically integrates structured KG metadata with unstructured vector retrieval is proposed. Firstly, a metadata-enriched KG was constructed from domain corpora by systematically extracting and indexing structured information to capture essential domain-specific relationships. Secondly, semantic alignment was achieved by injecting domain-specific constraints to refine and enhance the contextual relevance of the knowledge representations. Lastly, a layered hybrid retrieval strategy was employed that combined the explicit reasoning capabilities of the KG with the efficient search power of vector-based similarity methods, and the resulting outputs were integrated via prompt engineering to generate comprehensive, context-aware responses. Evaluated on design for additive manufacturing (DfAM) tasks, the proposed approach achieved 77.8% exact match accuracy and 76.5% context precision. This study establishes a new paradigm for industrial LLM systems, which demonstrates that hybrid symbolic-neural architectures can overcome the precision-scalability trade-off in mission-critical manufacturing applications. Experimental results indicated that integrating structured KG information with vector-based retrieval and prompt engineering can enhance retrieval accuracy, contextual relevance, and efficiency in LLM-based Q&A systems for SM.

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引用次数: 0

A novel reinforced incomplete cyber-physics ensemble with error compensation learning for within-batch quality prediction

IF 8 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2025-02-22 DOI: 10.1016/j.aei.2025.103172

Yi Shan Lee , Junghui Chen

This study addresses the challenge of real-time quality monitoring in batch operation by emphasizing the significance of within-batch quality estimation. While data-driven machine learning models are easy to construct, they often lack reliability and interpretability when dealing with sparse quality data. Conversely, first-principles models (FPMs) are interpretable but struggle with accuracy and adaptability to changing conditions. To overcome these issues, a three-phase reinforced incomplete cyber-physical ensemble plus error compensation learning (RICPE-P-ECL) method is proposed. This method enhances the adaptability of the incomplete cyber-physical model (IncompCPM), which relies on partially-available FPMs, for online quality prediction under varying conditions. The innovation in RICPE-P-ECL lies in its ensemble design and error compensation strategy. Phase 1 constructs IncompCPMs to predict quality for each operating condition, creating base models for ensemble learning. Phase 2 combines these IncompCPMs, with real-time information assigning weights to each model. Phase 3 involves an error compensation agent that adjusts the real-time ensemble prediction, addressing the limitations of FPMs and sparse data. The method is evaluated using a fed-batch bioreactor as the process model, and the results demonstrate that RICPE-P-ECL outperforms traditional data-driven models such as semi-supervised latent dynamic variational autoencoder and semi supervised dual attentioned latent dynamic complementary state space model, achieving R² values close to 1 for real-time within-batch quality prediction across five new testing conditions.

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引用次数: 0

Inverse design of lattice structures with target mechanical performance via generative adversarial networks considering the effect of process parameters

IF 8 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2025-02-21 DOI: 10.1016/j.aei.2025.103221

Chenglong Duan, Dazhong Wu

While generative artificial intelligence has been used to design materials and structures for additive manufacturing, current techniques can only generate design parameters. However, not only design parameters but also additive manufacturing (AM) process parameters affect the mechanical properties of additively manufactured materials. To address this issue, we introduce an auxiliary classifier generative adversarial network (ACGAN)-based computational framework that generates both design and AM process parameters to fabricate lattice structures with target mechanical performance. The computational framework consists of two ACGAN models, including a generative model called InverseACGAN and a forward predictive model called ForwardACGAN. The generative model generates critical design parameters of the lattice structures, including line distance, layer height, and infill pattern, as well as AM process parameters, including print speed and print temperature, based on target mechanical properties (i.e., porosity and compressive modulus). The forward predictive model predicts the mechanical properties of the lattice structures generated by the generative model. The experimental results show that the porosity and compressive modulus of the lattice structures designed by ACGAN are in good agreement with the target porosity and compressive modulus. The average mean absolute percentage errors between target and actual porosity, and target and actual compressive modulus are 6.481% and 10.208%, respectively.

虽然生成式人工智能已被用于设计增材制造材料和结构，但目前的技术只能生成设计参数。然而，不仅是设计参数，增材制造（AM）工艺参数也会影响增材制造材料的机械性能。为解决这一问题，我们引入了基于辅助分类器生成对抗网络（ACGAN）的计算框架，该框架可生成设计参数和增材制造工艺参数，从而制造出具有目标机械性能的晶格结构。该计算框架由两个 ACGAN 模型组成，包括一个称为反向 ACGAN 的生成模型和一个称为正向 ACGAN 的前向预测模型。生成模型根据目标机械性能（即孔隙率和压缩模量）生成晶格结构的关键设计参数，包括线距、层高和填充图案，以及 AM 工艺参数，包括打印速度和打印温度。前向预测模型可预测生成模型生成的晶格结构的机械性能。实验结果表明，ACGAN 设计的晶格结构的孔隙率和压缩模量与目标孔隙率和压缩模量非常吻合。目标孔隙率与实际孔隙率、目标压缩模量与实际压缩模量之间的平均绝对百分比误差分别为 6.481% 和 10.208%。

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引用次数: 0

An exact algorithm for RAP with k-out-of-n subsystems and heterogeneous components under mixed and K-mixed redundancy strategies

IF 8 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2025-02-20 DOI: 10.1016/j.aei.2025.103163

Jiangang Li , Dan Wang , Haoxiang Yang , Mingli Liu , Shubin Si

Redundancy design is a widely used technique for enhancing system reliability across various industries, including aerospace and manufacturing. Consequently, the redundancy allocation problem (RAP) has attracted considerable attention in the field of reliability engineering. The RAP seeks to determine an optimal redundancy scheme for each subsystem under resource constraints to maximize system reliability. However, existing RAP models and exact algorithms are predominantly confined to simple 1-out-of-n subsystems or single optimization strategies, thereby limiting the optimization potential and failing to adequately address the engineering requirements. This paper introduces a model and an exact algorithm for RAP with k-out-of-n subsystems and heterogeneous components under mixed and K-mixed redundancy strategies. The model employs a continuous time Markov chain method to calculate subsystem reliability exactly. A dynamic programming (DP) algorithm based on super component and sparse node strategies is designed to obtain the exact solution for RAP. Numerical experiments confirm that all benchmark test problems reported in the literature are exactly solved by the proposed DP. The experiment results demonstrate that the proposed RAP model offers high flexibility and potential for reliability optimization. Additionally, owing to the generality of the problem considered, the proposed DP also exactly solves other RAP models with 1-out-of-n subsystems and simplified redundancy strategies, which provides a more generalized framework for redundancy optimization. Finally, the research’s applicability in reliability engineering is validated through an optimization case study of a natural gas compressor pipeline system.

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引用次数: 0

Research on multi-step ahead prediction method for tool wear based on MSTCN-SBiGRU-MHA

IF 8 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2025-02-20 DOI: 10.1016/j.aei.2025.103219

Jing Xue, Yaonan Cheng, Wenjie Zhai, Xingwei Zhou, Shilong Zhou

Tool wear monitoring (TWM), as an important component of modern intelligent processing, faces significant challenges related to accuracy and long-term predictability. This research proposes a method for the precise and reliable multi-step prediction of tool wear. First, a dual-indicator feature screening scheme is proposed. The constructed sensitive features can describe the tool wear condition from multiple perspectives. Further, the MSTCN-SBiGRU-MHA model is developed to effectively analyze time series data by incorporating three key modules. The synergistic interaction among these three modules contributes to the model’s superior performance in complex time series prediction tasks. Finally, the multi-step prediction approach is integrated with interval prediction, and the validity of the resultant predictions is substantiated through milling experiments. Ablation experiments and the SHAP method are used to analyze the contribution of different modules and features to the model’s performance. Comparative experiments show that the model’s R2 for predicting the next 1, 5, and 10 steps across various datasets exceeded 0.86, significantly outperforming the SLSTM, SGRU, SBiLSTM-AT, and CNN-LSTM models. Accurate advance prediction of tool wear is crucial for developing an intelligent early warning system, ensuring high-quality production, reducing operational and maintenance costs, and enhancing machining safety.

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引用次数: 0

Knowledge transfer from simple to complex: A safe and efficient reinforcement learning framework for autonomous driving decision-making

IF 8 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2025-02-20 DOI: 10.1016/j.aei.2025.103188

Rongliang Zhou , Jiakun Huang , Mingjun Li , Hepeng Li , Haotian Cao , Xiaolin Song

A safe and efficient decision-making system is crucial for autonomous vehicles. However, the complexity of driving environments often limits the effectiveness of many rule-based and machine learning approaches. Reinforcement learning (RL), with its robust self-learning capabilities and adaptability to diverse environments, offers a promising solution. Despite this, concerns about safety and efficiency during the training phase have hindered its widespread adoption. To address these challenges, we propose a novel RL framework, Simple to Complex Collaborative Decision (S2CD), based on the Teacher–Student Framework (TSF) to facilitate safe and efficient knowledge transfer. In this approach, the teacher model is first trained rapidly in a lightweight simulation environment. During the training of the student model in more complex environments, the teacher evaluates the student’s selected actions to prevent suboptimal behavior. Besides, to enhance performance further, we introduce an RL algorithm called Adaptive Clipping Proximal Policy Optimization Plus (ACPPO+), which combines samples from both teacher and student policies while utilizing dynamic clipping strategies based on sample importance. This approach improves sample efficiency and mitigates data imbalance. Additionally, Kullback–Leibler (KL) divergence is employed as a policy constraint to accelerate the student’s learning process. A gradual weaning strategy is then used to enable the student to explore independently, overcoming the limitations of the teacher. Moreover, to provide model interpretability, the Layer-wise Relevance Propagation (LRP) technique is applied. Simulation experiments conducted in highway lane-change scenarios demonstrate that S2CD significantly enhances training efficiency and safety while reducing training costs. Even when guided by suboptimal teachers, the student consistently outperforms expectations, showcasing the robustness and effectiveness of the S2CD framework.

{"title":"Knowledge transfer from simple to complex: A safe and efficient reinforcement learning framework for autonomous driving decision-making","authors":"Rongliang Zhou , Jiakun Huang , Mingjun Li , Hepeng Li , Haotian Cao , Xiaolin Song","doi":"10.1016/j.aei.2025.103188","DOIUrl":"10.1016/j.aei.2025.103188","url":null,"abstract":"<div><div>A safe and efficient decision-making system is crucial for autonomous vehicles. However, the complexity of driving environments often limits the effectiveness of many rule-based and machine learning approaches. Reinforcement learning (RL), with its robust self-learning capabilities and adaptability to diverse environments, offers a promising solution. Despite this, concerns about safety and efficiency during the training phase have hindered its widespread adoption. To address these challenges, we propose a novel RL framework, Simple to Complex Collaborative Decision (S2CD), based on the Teacher–Student Framework (TSF) to facilitate safe and efficient knowledge transfer. In this approach, the teacher model is first trained rapidly in a lightweight simulation environment. During the training of the student model in more complex environments, the teacher evaluates the student’s selected actions to prevent suboptimal behavior. Besides, to enhance performance further, we introduce an RL algorithm called Adaptive Clipping Proximal Policy Optimization Plus (ACPPO+), which combines samples from both teacher and student policies while utilizing dynamic clipping strategies based on sample importance. This approach improves sample efficiency and mitigates data imbalance. Additionally, Kullback–Leibler (KL) divergence is employed as a policy constraint to accelerate the student’s learning process. A gradual weaning strategy is then used to enable the student to explore independently, overcoming the limitations of the teacher. Moreover, to provide model interpretability, the Layer-wise Relevance Propagation (LRP) technique is applied. Simulation experiments conducted in highway lane-change scenarios demonstrate that S2CD significantly enhances training efficiency and safety while reducing training costs. Even when guided by suboptimal teachers, the student consistently outperforms expectations, showcasing the robustness and effectiveness of the S2CD framework.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103188"},"PeriodicalIF":8.0,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143454467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Constraint programming-based layered method for integrated process planning and scheduling in extensive flexible manufacturing

IF 8 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2025-02-20 DOI: 10.1016/j.aei.2025.103210

Mengya Zhang, Xinyu Li, Liang Gao, Qihao Liu

Current extensive flexible manufacturing systems are characterized by high flexibility and large problem sizes, which present significant challenges to manufacturing efficiency. The integrated process planning and scheduling (IPPS) is a significant issue in this context. Due to the complexity of the integration problem, various approximation algorithms have been developed to tackle it, though this often demands considerable designer expertise and parameter tuning. This paper proposes a constraint programming (CP)-based method that can solve the large-scale IPPS problem in extensive flexible manufacturing. Firstly, this paper proposes a CP model which enriches the variable decision-making for flexible processes. Based on this, this paper presents a hybrid layered constraint programming (HLCP) method, which decomposes the complete CP model into multiple models of sub-problems and solves these models iteratively to reduce the solution difficulty. It contains multiple sets of model relaxation and repair stages. Experiments on benchmark instances confirm that the proposed method reaches all optimal solutions, and surpasses previous results on 9 instances. Next, the proposed methods are tested on 35 sets of large-scale instances with up to 8000 operations, and the results show that the minimum gap can be obtained compared to the existing methods. Moreover, the proposed HLCP method is able to reduce the gap by an average of 9.03% within a reasonable time compared to the single-model approach.

{"title":"Constraint programming-based layered method for integrated process planning and scheduling in extensive flexible manufacturing","authors":"Mengya Zhang, Xinyu Li, Liang Gao, Qihao Liu","doi":"10.1016/j.aei.2025.103210","DOIUrl":"10.1016/j.aei.2025.103210","url":null,"abstract":"<div><div>Current extensive flexible manufacturing systems are characterized by high flexibility and large problem sizes, which present significant challenges to manufacturing efficiency. The integrated process planning and scheduling (IPPS) is a significant issue in this context. Due to the complexity of the integration problem, various approximation algorithms have been developed to tackle it, though this often demands considerable designer expertise and parameter tuning. This paper proposes a constraint programming (CP)-based method that can solve the large-scale IPPS problem in extensive flexible manufacturing. Firstly, this paper proposes a CP model which enriches the variable decision-making for flexible processes. Based on this, this paper presents a hybrid layered constraint programming (HLCP) method, which decomposes the complete CP model into multiple models of sub-problems and solves these models iteratively to reduce the solution difficulty. It contains multiple sets of model relaxation and repair stages. Experiments on benchmark instances confirm that the proposed method reaches all optimal solutions, and surpasses previous results on 9 instances. Next, the proposed methods are tested on 35 sets of large-scale instances with up to 8000 operations, and the results show that the minimum gap can be obtained compared to the existing methods. Moreover, the proposed HLCP method is able to reduce the gap by an average of 9.03% within a reasonable time compared to the single-model approach.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103210"},"PeriodicalIF":8.0,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143454464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0