Computers in Industry最新文献

To make manufacturing technology productive, manufacturers rely on a production planning and control (PPC) framework that plans ahead and monitors ongoing transformation processes. The design of an appropriate framework has far-reaching implications for the manufacturing organization as a whole. Yet, to date, there has been no unified guidance on key PPC design issues. This is strongly needed, as it has been argued that novel information processing technologies – as part of Industry 4.0 – result in PPC frameworks with decentral structures. This conflicts with traditional works arguing for hierarchical or central structures. Therefore, we review the PPC design literature to create a comprehensive overview and summarize design proposals. Based on our review, we come to the intermediate conclusion that PPC frameworks continue to have a hierarchical structure, although decision-making is shifted more to decentral levels compared to traditional hierarchies. Our analysis suggests that the effect of a decentralization shift has potentially strong and poorly understood implications, both from a decision-making and organizational perspective.

In the manufacturing industry, unstructured documents such as design guidelines, regulatory documents, and failure cases are essential for product development. However, due to the large volume and frequent revisions of these documents, designers often find it difficult to keep up to date with the latest content. This study presents a method for analyzing the characteristics of unstructured design guidelines and automatically constructing a knowledgebase of design requirements from them. A knowledgebase is structured data that a computer can understand, and that can be used to assist designers in the design process. The knowledgebase is constructed using the sections of the document, including design variables and design requirements. The construction process involves pre-processing the documents, extracting information using natural language processing models, and generating a knowledgebase using predefined rules. A requirements knowledgebase was experimentally constructed from a standard document on the general requirements for the design of pressure vessels (American Society of Mechanical Engineers Section VIII Division 1) using the proposed method. In the experiment, the accuracy of information extraction was 86.3 %, and the generation process took 3 min and 50 s. Thus, the proposed method eliminates the need for specialized training of deep learning models and can be applied to various design guideline documents with simple modifications to the design vocabulary and rules. The knowledgebase has applications in design validation, and is expected to enhance the efficiency of the product development process and contribute to reducing the overall development timeline.

As a crucial component supporting aero-engine functionality, effective fault diagnosis of bearings is essential to ensure the engine's reliability and sustained airworthiness. However, practical limitations prevail due to the scarcity of aero-engine bearing fault data, hampering the implementation of intelligent diagnosis techniques. This paper presents a specialized method for aero-engine bearing fault diagnosis under conditions of limited sample availability. Initially, the proposed method employs the refined composite multiscale phase entropy (RCMPhE) to extract entropy features capable of characterizing the transient signal dynamics of aero-engine bearings. Based on the signal amplitude information, the composite multiscale decomposition sequence is formulated, followed by the creation of scatter diagrams for each sub-sequence. These diagrams are partitioned into segments, enabling individualized probability distribution computation within each sector, culminating in refined entropy value operations. Thus, the RCMPhE addresses issues prevalent in existing entropy theories such as deviation and instability. Subsequently, the bonobo optimization support vector machine is introduced to establish a mapping correlation between entropy domain features and fault types, enhancing its fault identification capabilities in aero-engine bearings. Experimental validation conducted on drivetrain system bearing data, actual aero-engine bearing data, and actual aerospace bearing data demonstrate remarkable fault diagnosis accuracy rates of 99.83 %, 100 %, and 100 %, respectively, with merely 5 training samples per state. Additionally, when compared to the existing eight fault diagnosis methods, the proposed method demonstrates an enhanced recognition accuracy by up to 28.97 %. This substantiates its effectiveness and potential in addressing small sample limitations in aero-engine bearing fault diagnosis.

The Knowledge-Based Digital Twin System is a digital twin system developed on the foundation of a knowledge graph, aimed at serving the complex manufacturing process. This system embraces a knowledge-driven modeling approach, aspiring to construct a digital twin model for the manufacturing process, thereby enabling precise description, management, prediction, and optimization of the process. The core of this system lies in the comprehensive knowledge graph that encapsulates all pertinent information about the manufacturing process, facilitating dynamic modeling and iteration through knowledge matching and inference within the knowledge, geometry, and decision model. This approach not only ensures consistency across models but also addresses the challenge of coupling multi-source heterogeneous information, creating a holistic and precise information model. As the manufacturing process deepens and knowledge accumulates, the model's understanding of the process progressively enhances, promoting self-evolution and continuous optimization. The developed knowledge-decision-geometry model acts as the ontological layer within the digital twin framework, laying a foundational conceptual framework for the digital twin of the manufacturing process. Validated on an aero-engine blade production line in a factory, the results demonstrate that the knowledge model, as the core driver, enables continuous self-updating of the geometric model for an accurate depiction of the entire manufacturing process, while the decision model provides deep insights for decision-makers based on knowledge. The system not only effectively controls, predicts, and optimizes the manufacturing process but also continually evolves as the process advances. This research offers a new perspective on the realization of the digital twin for the manufacturing process, providing solid theoretical support with a knowledge-driven approach.

Detecting leaks in natural gas gathering pipelines is paramount for the safe and reliable operation of the gas and oil industry. Due to the lack of leak data and the changes in leak features, semi-supervised leak detection methods that use normal data for health model learning have attracted much attention. However, these approaches usually consider one-class normal samples as health data, which may fail to fit the reality of unlabeled multi-class non-leak data under variable operating conditions. In addition, existing semi-supervised methods often suffer from insufficient representation learning as they employ step-by-step training or rely on the low-level reconstruction of autoencoders. To address the above two key challenges, this paper proposes a novel end-to-end self-supervised leak detection method, self-supervised multi-sphere support vector data description. Specifically, it utilizes the presented multi-sphere support vector data description to model unlabeled multi-class non-leak data and the introduced self-supervised learning strategy to boost the representation learning of the end-to-end semi-supervised model. Moreover, the categories of unlabeled multi-class non-leak data are learned in an unsupervised way through alternating feature clustering and pseudo-label-based classification. A robust leak score calculation method is also designed to improve the performance of the proposed method. Finally, the experimental results on the field data collected from pipelines demonstrate the effectiveness of the proposed method.

Accurate land valuation is crucial in sustainable urban development, influencing pivotal decisions on resource allocation and land-use strategies. Most existing studies, primarily using point-based modeling approaches, face challenges on granularity, generalizability, and spatial effect capturing, limiting their effectiveness in regional land valuation with high granularity. This study therefore proposes the LVGAN (i.e., land value generative adversarial networks) framework for regional land value estimation. The LVGAN model redefines land valuation as an image generation task, employing deep generative techniques combined with attention mechanisms to forecast high-resolution relative value distributions for informed decision-making. Applied to a case study of New York City (NYC), the LVGAN model outperforms typical deep generative methods, with MAE (Mean Absolute Error) and MSE (Mean Squared Error) averagely reduced by 36.58 % and 59.28 %, respectively. The model exhibits varied performance across five NYC boroughs and diverse urban contexts, excelling in Manhattan with limited value variability, and in areas characterized by residential zoning and high density. It identifies influential factors such as road network, built density, and land use in determining NYC land valuation. By enhancing data-driven decision-making at early design stages, the LVGAN model can promote stakeholder engagement and strategic planning for sustainable and well-structured urban environments.

Anomaly detection with high accuracy, recall, and low error rate is critical for the safe and uninterrupted operation of cyber-physical systems. However, detecting anomalies in multimodal time series with different modalities obtained from cyber-physical systems is challenging. Although deep learning methods show very good results in anomaly detection, they fail to detect anomalies according to the requirements of cyber-physical systems. In the use of graph-based methods, data loss occurs during the conversion of time series into graphs. The fixed window size used to transform time series into graphs causes a loss of spatio-temporal correlations. In this study, we propose an Event Aware Graph Attention Network (EA-GAT), which can detect anomalies by event-based cyber-physical system analysis. EA-GAT detects and tracks the sensors in cyber-physical systems and the correlations between them. The system analyzes and models the relationship between the components during the marked periods as a graph. Anomalies in the system are found through the created graph models. Experiments show that the EA-GAT technique is more effective than other deep learning methods on SWaT, WADI, MSL datasets used in various studies. The event-based dynamic approach is significantly superior to the fixed-size sliding window technique, which uses the same learning structure. In addition, anomaly analysis is used to identify the attack target and the affected components. At the same time, with the slip subsequence module, the data is divided into subgroups and processed simultaneously.

Protecting intellectual property rights (IPR) in the architecture, engineering, and construction (AEC) industry is a long-standing challenge. In the collaborative digital environments, where multiple professionals use digital platforms such as building information modelling to collaborate on a building design, this challenge has intensified. This research harnesses the functions of blockchain technology, such as consensus mechanisms, distributed broadcasting ledgers, cryptographic algorithms, and non-fungible tokens, to propose a blockchain-based framework to protect building design IPR in the AEC industry. Adopting a design science approach, a framework is proposed and then further developed into a system that is implemented, illustrated, and evaluated in a case study. The system uses non-fungible tokens to tokenize building design IPR and deploys blockchain’s decentralized consensus mechanisms, distributed ledgers, and cryptographic algorithms to safeguard the IPR and its transactions. This prototype system is found feasible with satisfactory performance in enhancing the efficiency of IPR registration and protection, reducing cost, improving information transparency, reinforcing immutability, and preventing non-valuable registrations. Researchers and practitioners are encouraged to develop the framework for different applications such as real-life design IPR protection and design management.

The purpose of this study is twofold: investigating how omnichannel (OC) retailers manage e-fulfillment costs and establishing how these costs relate to the evolution of OC retailers' e-fulfillment strategies. Experts in e-fulfillment from 34 European OC retailers across various sectors participated in an exploratory survey. The study's results reveal that although e-fulfillment costs significantly influence the evolution of e-fulfillment strategies, many OC retailers fulfilling online orders from retail stores or traditional warehouses remain unaware of the actual costs of e-fulfillment. Activities other than picking and last-mile delivery, such as inbound logistics and storage, are poorly controlled. Furthermore, complex cost metrics such as cost-to-serve—the total cost associated with delivering a specific order to a specific customer—are predominantly found among OC retailers operating fulfillment centers (FCs) in their e-fulfillment distribution networks. This underscores the need for all OC retailers to accurately assess e-fulfillment costs at multiple levels, which will be crucial for optimizing order preparation, tailoring pricing strategies, and achieving profitability, especially when operating hybrid e-fulfillment strategies where online orders are prepared in multiple facilities. As the largest study on e-fulfillment costs to date, it highlights the importance of advancing e-fulfillment cost management systems among OC retailers and adopting an approach that encompasses all e-fulfillment activities. Future research should delve into the key challenges of developing these systems, considering the operational realities of each OC retailer.

In the realm of smart manufacturing, Augmented Reality (AR) technology has gained increasing attention among researchers and manufacturers due to its practicality and adaptability. For this reason, it has been widely embraced in various industrial fields, especially for helping operators assemble products. Despite its widespread adoption, there is a debate in the research community about how effective AR is for improving user performance in assembly tasks, particularly when using handheld devices. These disparities can be attributed to differences in experimental approaches, such as the frequent use of qualitative methods, the inclusion of non-representative users, and the limited number of comprehensive case studies.

In response to this, the paper delved into the benefits of AR applications, with a specific focus on measuring user performance and the cognitive workload perceived by users during assembly activities. To this end, an AR assembly guidance tool has been developed to assist users during assembly tasks, running on a mobile device, specifically a tablet, for freedom of movement and high portability. Experimentation involved the assembly of a comprehensive case study and a diverse user group, allowing the comparison representative users and experienced industrial operators. The results were promising, indicating that AR technology effectively enhances user performance during assembly-guided activities compared to conventional methods, particularly when users are unfamiliar with the task at hand. This study brings valuable insights by addressing previous research limitations and providing strong evidence of AR's positive impact on user performance in real-world assembly scenarios.