Article
Computer Science, Interdisciplinary Applications
Sjoerd Rongen, Nikoletta Nikolova, Mark van der Pas
Summary: Industry 4.0 introduces the Asset Administration Shell (AAS) model for digital twins to address interoperability issues, which can also be tackled by the Semantic Web and its Resource Description Framework (RDF). Both AAS and RDF-based models have their own strengths, with AAS models being easier to integrate with operational technologies and RDF-based models offering more semantic expressiveness and advanced querying.
COMPUTERS IN INDUSTRY
(2023)
Article
Business
Marco Paiola, Francesco Schiavone, Tatiana Khvatova, Roberto Grandinetti
Summary: This article investigates the roles and effects of prior technological knowledge on business models for digital servitization. Findings from multiple Italian enterprise case studies show that firms' past experience and knowledge significantly affect their digital servitization strategies, leading to the identification of four distinct ideal-typical business models.
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2021)
Article
Automation & Control Systems
Mirko Mazzoleni, Kisan Sarda, Antonio Acernese, Luigi Russo, Leonardo Manfredi, Luigi Glielmo, Carmen Del Vecchio
Summary: In this paper, a diagnostic scheme for condition monitoring of mechanical components is proposed, combining anomaly detection algorithms, envelope analysis of vibration data, and additional qualitative information on machine functioning. The combination of all fault indicators is obtained using a fuzzy inference system.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Review
Chemistry, Multidisciplinary
Muhammad Yahya, John G. Breslin, Muhammad Intizar Ali
Summary: This paper explores the use of Semantic Web and Knowledge Graphs in Industry 4.0, proposing an enhanced reference generalized ontological model based on the Reference Architecture Model for I4.0. This model can facilitate various I4.0 concepts and enable the generation of a knowledge graph to provide real-time query responses.
APPLIED SCIENCES-BASEL
(2021)
Article
Automation & Control Systems
B. Natesha, Ram Mohana Reddy Guddeti
Summary: This article discusses the method of diagnosing machine faults in industrial environments using fog computing architecture, judging the normal and abnormal states of machines through their operating sounds and monitoring.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Computer Science, Information Systems
Chunhe Song, Shuo Liu, Guangjie Han, Peng Zeng, Haibin Yu, Qingyuan Zheng
Summary: This article discusses the importance of accurately monitoring the operating state and detecting faults of beam pumping units in Industrial Internet of Things systems. It proposes methods for period estimation, denoising, and fault detection to improve accuracy and efficiency. Experimental results confirm the effectiveness of the proposed approach.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Engineering, Industrial
Luca Pinciroli, Piero Baraldi, Enrico Zio
Summary: This paper provides a comprehensive review of maintenance optimization from different and complementary perspectives. It analyzes the knowledge, information, and data that can be used for maintenance optimization within the Industry 4.0 paradigm. The paper discusses the objectives of optimization, maintenance features to be optimized, challenges, and trends, emphasizing the need for methods that can handle heterogeneous data, uncertainties, and multiple optimization objectives.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Engineering, Electrical & Electronic
Ziaur Rahman, Ibrahim Khalil, Xun Yi, Mohammed Atiquzzaman
Summary: Amidst the recent surge in cyber attacks targeting critical systems such as industry, medical, and energy ecosystems, a unique blockchain-based framework has been introduced to address the shortcomings of traditional cloud or trusted-certificate-driven techniques, enhancing security and efficiency for Industry 4.0 systems. The demonstrated framework eliminates the longstanding certificate authority, reduces data processing delay, and increases cost-effective throughput, while promoting cooperative trust and multi-party authentication in the distributed Industry 4.0 security model.
IEEE COMMUNICATIONS MAGAZINE
(2021)
Article
Computer Science, Artificial Intelligence
Han Xiao, Yidong Chen, Xiaodong Shi
Summary: Knowledge representation is a critical issue in knowledge engineering and artificial intelligence, with knowledge embedding methods playing an important role. This paper introduces a semantic model based on multi-view clustering for generating semantic representations of knowledge elements and improving entity retrieval. Extensive experiments demonstrate substantial improvements of this model against baselines on various knowledge graph tasks.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2021)
Article
Computer Science, Interdisciplinary Applications
Qiushi Cao, Cecilia Zanni-Merk, Ahmed Samet, Christoph Reich, Francois de Bertrand de Beuvron, Arnold Beckmann, Cinzia Giannetti
Summary: In the context of Industry 4.0, smart factories utilize advanced technologies for production monitoring and analysis, but the heterogeneous nature of industrial data leads to complex knowledge extraction. In order to achieve predictive maintenance, symbolic AI technologies are required. KSPMI is a knowledge-based system developed based on a hybrid approach utilizing both statistical and symbolic AI technologies.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2022)
Article
Engineering, Multidisciplinary
Ambarish Gajendra Mohapatra, Anita Mohanty, Nihar Ranjan Pradhan, Sachi Nandan Mohanty, Deepak Gupta, Meshal Alharbi, Ahmed Alkhayyat, Ashish Khanna
Summary: Diesel generators are a reliable backup power source, and their efficiency can be improved by monitoring key machine parameters. Traditional equipment maintenance methods have been replaced by IoT-based remote monitoring systems. This paper introduces a remote monitoring and data acquisition scheme for predictive maintenance, discusses a strategy for real-time observation and comprehensive analysis of DG parameters, and includes a monitoring and analysis scheme for crucial factors such as engine speed, voltage output, current production, power factor, coolant requirement, fuel consumption, and battery health.
ALEXANDRIA ENGINEERING JOURNAL
(2023)
Article
Computer Science, Hardware & Architecture
Jesus N. S. Rubi, Paulo H. P. de Carvalho, Paulo R. L. Gondim
Summary: This research focuses on the application of Internet of Forest Things (IoFT) in predicting wildfire behavior, and proposes a semantic platform for aggregating heterogeneous data and achieving interoperability through semantic technologies. By using machine learning techniques, the study predicted the areas affected after a fire event based on climatic-and vegetation-related data gathered by Brazilian government sensors and satellite information. The validation showed the effectiveness of the proposed platform and predictions.
COMPUTERS & ELECTRICAL ENGINEERING
(2022)
Article
Engineering, Manufacturing
Chunguang Bai, H. Alice Li, Yongbo Xiao
Summary: This study empirically analyzes the impact of investing in Industry 4.0 technologies in China based on a sample of investment announcements from publicly listed firms. The findings show that these investments lead to positive stock market reactions and improved financial performance. The study also proposes a decision framework for firms to balance short-term disruption and long-term benefits.
PRODUCTION AND OPERATIONS MANAGEMENT
(2022)
Article
Computer Science, Information Systems
Anil Verma, Divya Anand, Aman Singh, Rishika Vij, Abdullah Alharbi, Majid Alshammari, Arturo Ortega Mansilla
Summary: Education 4.0 imitates Industry 4.0 by adopting advanced technologies such as IoT, Fog Computing, Cloud Computing, and AR/VR. This study proposes a reliable assessment, irregularity detection, and alert generation framework for Education 4.0, which addresses similar issues faced in Industry 4.0. Experimental simulations validate the superior performance of the proposed framework compared to other contemporary technologies used in Education 4.0.
Article
Computer Science, Artificial Intelligence
Jiaoyan Chen, Ernesto Jimenez-Ruiz, Ian Horrocks, Xi Chen, Erik Bryhn Myklebust
Summary: This study proposes a general correction framework to effectively correct erroneous assertions and alignments, achieving promising results through methods such as lexical matching, context-aware sub-KB extraction, semantic embedding, soft constraint mining, and semantic consistency checking.
