Advanced Engineering Informatics最新文献

{"title":"Knowledge augmented generalizer specializer: A framework for early stage design exploration","authors":"Vijayalaxmi Sahadevan ,&nbsp;Rohin Joshi ,&nbsp;Kane Borg ,&nbsp;Vishal Singh ,&nbsp;Abhishek Raj Singh ,&nbsp;Bilal Muhammed ,&nbsp;Soban Babu Beemaraj ,&nbsp;Amol Joshi","doi":"10.1016/j.aei.2025.103141","DOIUrl":"10.1016/j.aei.2025.103141","url":null,"abstract":"<div><div>In non-routine engineering design projects, the design outcome is determined by how the problem is formulated and represented in the early conceptual stage. The problem representation comprises schemas, ontologies, variables, and parameters relevant to the given problem class. Despite the critical role of early conceptual decisions in shaping the eventual design outcome, most of the computational support and automation are focused on the latter stages of parametric modelling, problem-solving, and optimization. There is inadequate support for aiding and automating problem formulation, variable and parameter identification and representation, and early-stage conceptual decisions. Therefore, this paper presents an innovative, transparent, and explainable method employing semantic reasoning to automate the step-by-step conceptual design generation process, including problem formulation, identification and representation of the variables and parameters and their dependencies. The method is realized through a novel framework called Knowledge Augmented Generalizer Specializer (KAGS). KAGS employs the Function-Behavior-Structure (FBS) ontology and the Graph-of-Thought (GoT) mechanism to enable automated reasoning with a Large Language Model (LLM). The workflow comprises various stages: problem breakdown, design prototype creation, assessment, and prototype merging. The framework is implemented and tested on a Subsea Layout (SSL) planning problem, a special class of infrastructure planning projects in deep-sea oil and gas production systems. The experimentations with KAGS demonstrate its capacity to support problem formulation, hierarchical decomposition, and solution generation. The research also provides new insights into the FBS framework and <em>meta</em>-level reasoning in early design stages.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103141"},"PeriodicalIF":8.0,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136971","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}

{"title":"Dual adversarial and contrastive network for single-source domain generalization in fault diagnosis","authors":"Guangqiang Li ,&nbsp;M. Amine Atoui ,&nbsp;Xiangshun Li","doi":"10.1016/j.aei.2025.103140","DOIUrl":"10.1016/j.aei.2025.103140","url":null,"abstract":"<div><div>Domain generalization achieves fault diagnosis on unseen modes. In process industrial systems, fault samples are limited, and it is quite common that the available fault data are from a single mode. Extracting domain-invariant features from single-mode data for unseen mode fault diagnosis poses challenges. Existing methods utilize a generator module to simulate samples of unseen modes. However, multi-mode samples contain complex spatiotemporal information, which brings significant difficulties to accurate sample generation. To solve this problem, this paper proposed a dual adversarial and contrastive network (DACN) for single-source domain generalization in fault diagnosis. The main idea of DACN is to generate diverse sample features and extract domain-invariant feature representations. An adversarial pseudo-sample feature generation strategy is developed to create fake unseen mode sample features with sufficient semantic information and diversity, leveraging adversarial learning between the feature transformer and domain-invariant feature extractor. An enhanced domain-invariant feature extraction strategy is designed to capture common feature representations across multi-modes, utilizing contrastive learning and adversarial learning between the domain-invariant feature extractor and the discriminator. Experiments on the Tennessee Eastman process and continuous stirred-tank reactor demonstrate that DACN achieves high classification accuracy on unseen modes while maintaining a small model size.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103140"},"PeriodicalIF":8.0,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143137038","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}

{"title":"Collaborative scheduling of handling equipment in automated container terminals with limited AGV-mates considering energy consumption","authors":"Xurui Yang ,&nbsp;Hongtao Hu ,&nbsp;Chen Cheng","doi":"10.1016/j.aei.2025.103133","DOIUrl":"10.1016/j.aei.2025.103133","url":null,"abstract":"<div><div>AGV-mates (automated guided vehicle, AGV) are a type of buffering equipment installed in the seaside area of the yard block, which can decouple AGV and yard crane operations. In recent years, an AGV charging function has been integrated in AGV-mates, providing AGVs with an alternative charging method besides battery recovery at the battery swapping station. This has resulted in time constraints and additional energy replenishment decisions in collaborative scheduling optimization, complicating the terminal equipment scheduling problem. Therefore, this paper investigates the collaborative scheduling problem of yard equipment in each operation stage of an automated container terminal, proposes charging-swapping mode for AGV energy replenishment, and develops a mixed integer programming model to minimize equipment no-load energy consumption and operational delay costs. In order to address the difficulty of solving large-scale cases, a solution method based on the variable neighborhood search algorithm is developed. Considering the decoupling and charging characteristics of AGV-mates, local search operators for the AGVs’ task sequence, the yard crane’s task sequence, and the AGV battery swapping task nodes are designed. Finally, the efficiency and effectiveness of proposed solution and operators are verified through a series of numerical experiments. This paper presents practical equipment scheduling solutions and management strategies, compared to a single charging or swapping mode, the charging-swapping mode proposed in this paper has a significant improvement in the no-load cost and the delay cost.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103133"},"PeriodicalIF":8.0,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143137074","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}

{"title":"Fractional-order PID-based search algorithms: A math-inspired meta-heuristic technique with historical information consideration","authors":"Guangyao Chen ,&nbsp;Yangze Liang ,&nbsp;Ziyang Jiang ,&nbsp;Sihao Li ,&nbsp;Heng Li ,&nbsp;Zhao Xu","doi":"10.1016/j.aei.2024.103088","DOIUrl":"10.1016/j.aei.2024.103088","url":null,"abstract":"<div><div>The PID-based Search Algorithm (PSA) is a novel math-inspired metaheuristic algorithm. However, the traditional PSA, based on PID principles, only considers the current population information. To investigate the influence of Population Historical Information (PHI) on the convergence performance of PSA and design a more effective population evolution mechanism, we drew inspiration from the fractional-order PID and introduced the fractional-order Nabla operator, which is well-suited for modeling discrete systems characterized by memory and heredity, to improve PSA. We proposed three fractional-order variants of PSA, named FoPSA-I, FoPSA-II, and FoPSA-III, based on three types of historical information in the population update process: error, input, and position. Through fractional-order sensitivity analysis on CEC benchmark test functions and numerical experiments in relevant engineering applications, we found that among the three FoPSA variants, FoPSA-III, which considers historical position information, showed significant differences in convergence performance compared to PSA, whereas FoPSA-I and FoPSA-II showed minimal differences from PSA. Additionally, the <em>p</em>-values obtained from the Wilcoxon test further validated the differences among the three FoPSAs and PSA, with <em>p</em>-values for FoPSA-I, FoPSA-II, and FoPSA-III being 0.1446, 0.0475, and 0.0019, respectively. Finally, through mathematical analysis, we qualitatively explored the reasons for the differing convergence performance of the three FoPSA variants. The results indicate that considering historical position information in the PSA population update process can enhance population diversity and the algorithm’s convergence performance. This provides new insights into the design of population update mechanisms in metaheuristic algorithms.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103088"},"PeriodicalIF":8.0,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143137076","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}

{"title":"A collaborative adversarial framework: Distribution characteristics-guided alignment mechanism for fault diagnosis of machines considering domain shift","authors":"Xiaoxuan Fan ,&nbsp;Lixiang Duan ,&nbsp;Na Zhang ,&nbsp;Mingyu Shen","doi":"10.1016/j.aei.2025.103159","DOIUrl":"10.1016/j.aei.2025.103159","url":null,"abstract":"<div><div>Fault diagnosis of mechanical systems is essential to minimize damage and downtime in industrial fields. These systems frequently operate under varying and harsh conditions, leading to substantial changes in data distributions, commonly referred to as domain shift problem. This phenomenon presents a significant challenge for reliable fault diagnosis. Although many unsupervised domain adaptation methods effectively align data distributions, they often depend on target-domain pseudo labels. This dependency may lead to inaccurate diagnoses, particularly in the presence of abnormal samples. To address this limitation, a collaborative adversarial framework is proposed to exploit the intrinsic distribution characteristics of mechanical vibration signals to achieve distribution alignment. This framework introduces a two-level adversarial strategy to reduce distribution discrepancies. At the domain level, a novel Domain Alignment Loss (DAL) is designed to guide the adversarial game between the feature generator and the domain discriminator, thereby reducing marginal distribution discrepancies by considering both the amplitude and variability of vibration signals. At the class level, a new Class Alignment Loss (CAL) is proposed to steer the adversarial game between the feature generator and the two classifiers, using Gaussian Mixture Models (GMM) and Reproducing Kernel Hilbert Space (RKHS) to provide a more accurate measurement of conditional distribution discrepancies. Results on two datasets show that the proposed method achieves superior alignment capability and higher diagnostic accuracy compared to other state-of-the-art methods.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103159"},"PeriodicalIF":8.0,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143137073","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}

{"title":"Rainflow evolution model: A holistic method of complex product functional design","authors":"Chuan He ,&nbsp;Mingyan Zhao ,&nbsp;Runhua Tan","doi":"10.1016/j.aei.2025.103162","DOIUrl":"10.1016/j.aei.2025.103162","url":null,"abstract":"<div><div>Product complexity increases along with the increase of product functions. Effective methods are required in complex product development. Axiomatic design can reduce product complexity, but there are deficiencies in innovation improvement. A rainflow evolution model is proposed based on the technological evolution law and scientific effect: it can realize the innovative improvement of complex product functional design processes. Firstly, the functional design direction and the corresponding substitutability technical knowledge are mined for complex products. Then, the technological evolution law and su-field models are introduced to construct the field-combination selection matrix, which defines the design path of the new structure. The scientific effects are retrieved based on the conversion of energy forms, and a new scheme is designed using the analogy method. The introduction of cross-domain knowledge for complex product improves the innovation of functional design. The proposed method is applied to develop a powder dryer machine, to prove its feasibility and effectiveness.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103162"},"PeriodicalIF":8.0,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143137072","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}

{"title":"A multi-aircraft co-operative trajectory planning model under dynamic thunderstorm cells using decentralized deep reinforcement learning","authors":"Bizhao Pang ,&nbsp;Xinting Hu ,&nbsp;Mingcheng Zhang ,&nbsp;Sameer Alam ,&nbsp;Guglielmo Lulli","doi":"10.1016/j.aei.2025.103157","DOIUrl":"10.1016/j.aei.2025.103157","url":null,"abstract":"<div><div>Climate change induces an increased frequency of adverse weather, particularly thunderstorms, posing significant safety and efficiency challenges in en route airspace, especially in oceanic regions with limited air traffic control services. These conditions require multi-aircraft cooperative trajectory planning to avoid both dynamic thunderstorms and other aircraft. Existing literature has typically relied on centralized approaches and single-agent principles, which lack coordination and robustness when surrounding aircraft or thunderstorms change paths, leading to scalability issues due to heavy trajectory regeneration needs. To address these gaps, this paper introduces a multi-agent cooperative method for autonomous trajectory planning. The problem is modeled as a Decentralized Markov Decision Process (DEC-MDP) and solved using an Independent Deep Deterministic Policy Gradient (IDDPG) learning framework. A shared actor-critic network is trained using combined experiences from all aircraft to optimize joint behavior. During execution, each aircraft acts independently based on its own observations, with coordination ensured through the shared policy. The model is validated through extensive simulations, including uncertainty analysis, baseline comparisons, and ablation studies. Under known thunderstorm paths, the model achieved a 2 % loss of separation rate, increasing to 4 % with random storm paths. ETA uncertainty analysis demonstrated the model’s robustness, while baseline comparisons with the Fast Marching Tree and centralized DDPG highlighted its scalability and efficiency. These findings contribute to advancing autonomous aircraft operations.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103157"},"PeriodicalIF":8.0,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143137037","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}

{"title":"A study on connectivity path search in fractured-vuggy reservoirs based on multi-agent system","authors":"Wenbin Jiang,&nbsp;Dongmei Zhang,&nbsp;Ruiqi Wang,&nbsp;Zhenkun Zhang","doi":"10.1016/j.aei.2025.103160","DOIUrl":"10.1016/j.aei.2025.103160","url":null,"abstract":"<div><div>Due to the complex and heterogeneous spatial structure of fractured-vuggy reservoirs caused by multiple tectonic movements, understanding inter-well connectivity paths is challenging. Explicit connectivity paths are crucial for designing injection and production schemes and enhancing oil recovery. Traditional methods often fail to adequately characterize geological structures, making it difficult to represent preferential pathways. This study proposes a novel algorithm that integrates seismic multi-attribute data and reinforcement learning to automatically search for 3D inter-well connectivity paths. A multi-agent deep reinforcement learning model based on actor-critic is employed, with each agent representing a flow direction in the multi-phase carbonate rock system. Game theory is used to identify connectivity paths that align with geological structures, while fluid flow laws are incorporated into the reward function to improve search accuracy. A multi-head self-attention mechanism is introduced to capture global state information and the correlation between fluid flows in different directions. Variational Bayesian estimation is utilized to improve search efficiency by leveraging prior geological data. The algorithm is applied to a typical oilfield in China, where it successfully identifies connectivity paths. The results are validated by comparing the identified paths with tracer concentration production curves, showing improved accuracy in representing the spatial distribution characteristics of the reservoir.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103160"},"PeriodicalIF":8.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143137069","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}

{"title":"Transparent information fusion network: An explainable network for multi-source bearing fault diagnosis via self-organized neural-symbolic nodes","authors":"Qi Li ,&nbsp;Lichang Qin ,&nbsp;Haifeng Xu ,&nbsp;Qijian Lin ,&nbsp;Zhaoye Qin ,&nbsp;Fulei Chu","doi":"10.1016/j.aei.2025.103156","DOIUrl":"10.1016/j.aei.2025.103156","url":null,"abstract":"<div><div>In recent years, the integration of Artificial Intelligence (AI) into Intelligent Fault Diagnosis (IFD) through multi-source signal fusion has advanced significantly. However, the inherent opacity of AI-driven IFD models often hampers explainability, a crucial factor for fostering deeper collaboration between humans and intelligent systems to enhance the safety and reliability of industrial assets. This study introduces a novel and explainable methodology termed the Transparent Information Fusion Network (TIFN). TIFN incorporates multiple self-organized Neural-Symbolic Nodes (NSNs) equipped with signal processing and statistical operators, as opposed to conventional black-box neural networks or manually crafted expert systems. NSNs are interconnected through learnable signal-wise gates to construct the Signal Operator Layer (SOL) and Feature Operator Layer (FOL). The entire network is trainable using gradient-based learning methods in a self-organized manner. This enables accurate representation of fault signals and establishes a fully comprehensible semantic framework with an explainable decision-making process. The transparency, generalization, and capability to learn from limited samples of TIFN are demonstrated through two case studies on rotating machinery equipped with different sensors. Case 1 fuses vibration and piezoelectric signals, while Case 2 integrates piezoelectric and triboelectric signals to achieve comprehensive information fusion at both signal and feature levels. By incorporating learnable NSNs, signal-wise gates, TIFN achieves superior diagnostic performance with fewer parameters compared to traditional expert-organized models. This research underscores the potential of TIFN as a fully explainable tool for industrial diagnostics with multi-source signals, paving the way for enhanced human-machine collaboration in Industry 5.0, with a focus on trustworthiness, transparency, and accountability.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103156"},"PeriodicalIF":8.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143137071","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}

{"title":"Enhancing novel product iteration: An integrated framework for heuristic ideation via interpretable conceptual design knowledge graph","authors":"Yangfan Cong ,&nbsp;Suihuai Yu ,&nbsp;Jianjie Chu ,&nbsp;Yuexin Huang ,&nbsp;Ning Ding ,&nbsp;Cong Fang ,&nbsp;Stephen Jia Wang","doi":"10.1016/j.aei.2025.103131","DOIUrl":"10.1016/j.aei.2025.103131","url":null,"abstract":"<div><div>Novel products emerge over time to survive the competitive landscape as no existing product can perpetually satisfy all evolving customer expectations. These products are often characterized by groundbreaking solutions previously unavailable on the market. However, the swift imitation of successful novel products by competitors underscores the need for sustained iteration and continuous improvement. Designers increasingly face challenges in keeping up to date with the growing volume and fragmented nature of design information from diverse sources. While knowledge graphs show promise in structuring and organizing complex design information, their effective application in the ideation process remains limited due to difficulties in automatic knowledge extraction and the lack of interpretability aligned well with designers’ cognitive processes. This study proposes an integrated method to construct an interpretable conceptual design knowledge graph (I-CDKG) that features both inherent and acquired interpretability for heuristic product ideation. First, the schema layer models product design knowledge and governs the semantic connection of design information reinforced by design cognition principles to create a reasonable organizational framework to foster intuitive knowledge exploration. Second, the data layer mainly fulfills automatic and smooth design knowledge extraction for I-CDKG construction through the deep learning ERNIE-BiGRU-CRF model combined with BIESO labeling mode and triple-extracting algorithm. Third, the application layer empowers designers to visually delve into interpretable design knowledge to locate inspiration from cluster, relation, and nest levels and enable constant I-CDKG expansion as design schemes proliferate. A case study on the smart cat litter box demonstrates the feasibility of the proposed methodology. The evaluation results confirm the I-CDKG’s advantages as a productive design tool for inspiring creative, practical, and cost-effective product ideations, thereby empowering the iterative development of competitive novel products.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103131"},"PeriodicalIF":8.0,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143137070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}