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Framework and case study of cognitive maintenance in Industry 4.0 Special Feature on Industrial Internet

Bao-rui Li, Yi Wang, Guo-hong Dai, Ke-sheng Wang,kesheng.wang@ntnu.no

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 11,   Pages 1493-1504 doi: 10.1631/FITEE.1900193

Abstract: We present a new framework for (CM) based on cyber-physical systems and advanced artificial intelligence techniques. These CM systems integrate intelligent approaches and intelligent decision-making techniques, which can be used by maintenance professionals who are working with . The systems will provide technical solutions to real-time online maintenance tasks, avoid outages due to equipment failures, and ensure the continuous and healthy operation of equipment and manufacturing assets. The implementation framework of CM consists of four modules, i.e., cyber-physical system, Internet of Things, data mining, and Internet of Services. In the data mining module, fault diagnosis and prediction are realized by methods. In the case study, the backlash error of cutting-edge machine tools is taken as an example. We use a deep belief network to predict the backlash of the machine tool, so as to predict the possible failure of the machine tool, and realize the strategy of CM. Through the case study, we discuss the significance of implementing CM for cutting- edge equipment, and the framework of CM implementation has been verified. Some CM system applications in manufacturing enterprises are summarized.

Keywords： 认知维护；工业4.0；尖端设备；深度学习；绿色监视器；智能制造工厂    

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Technological and Economical Logic for Intelligent Manufacturing

Guo Zhaohui

Strategic Study of CAE 2018, Volume 20, Issue 4,   Pages 97-100 doi: 10.15302/J-SSCAE-2018.04.016

Abstract:

Focusing on the difficulties domestic industrial enterprises are facing while promoting intelligent manufacturing, this article introduces the methods for facilitating in-depth application of related technologies. It is shown through research and comparison, that the fundamental cause of these difficulties is the poor economic efficiency of technology. Therefore, the key to promote the application of certain technology is to improve the economic efficiency of technology. Besides, problems such as out-dated management levels and low requirements for quality are the general causes restraining the economic efficiency of technology. To promote the economic efficiency of technology, strategic goals have to be set for technological transformation and upgrading on a business level, and a higher quality standard should also be set. Meanwhile, intelligent manufacturing related technologies can be used to promote the management level and to address the weaknesses of the enterprises, thus to improve the economic efficiency of technology.

Keywords： smart manufacture     industrial Internet     Industrial 4.0     Cyberspace    

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Intelligent Manufacturing in the Context of Industry 4.0: A Review

Ray Y. Zhong, Xun Xu, Eberhard Klotz, Stephen T. Newman

Engineering 2017, Volume 3, Issue 5,   Pages 616-630 doi: 10.1016/J.ENG.2017.05.015

Abstract:

Our next generation of industry—Industry 4.0—holds the promise of increased flexibilityIntelligent manufacturing plays an important role in Industry 4.0.In order to fully understand intelligent manufacturing in the context of Industry 4.0, this paper provides

Keywords： Intelligent manufacturing     Industry 4.0     Internet of Things     Manufacturing systems     Cloud manufacturing     Cyber-physical    

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Opportunities and Challenges of Artificial Intelligence for Green Manufacturing in the Process Industry

Shuai Mao, Bing Wang, Yang Tang, Feng Qian

Engineering 2019, Volume 5, Issue 6,   Pages 995-1002 doi: 10.1016/j.eng.2019.08.013

Abstract:

Smart  manufacturing is critical in improving the quality of the process industry. In smart manufacturing, there is a trend to incorporate different kinds of new-generation information technologies into process-safety analysis. At present, green manufacturing is facing major obstacles related to safety management, due to the usage of large amounts of hazardous chemicals, resulting in spatial inhomogeneity of chemical industrial processes and increasingly stringent safety and environmental regulations. Emerging information technologies such as artificial intelligence (AI) are quite promising as a means of overcoming these difficulties. Based on state-of-the-art AI methods and the complex safety relations in the process industry, we identify and discuss several technical challenges associated with process safety: ① knowledge acquisition with scarce labels for process safety; ② knowledge-based reasoning for process safety; ③ accurate fusion of heterogeneous data from various sources; and ④ effective learning for dynamic risk assessment and aided decision-making. Current and future works are also discussed in this context.

Keywords： Process industry     Smart manufacturing     Green manufacturing     Artificial intelligence    

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Greenization, Intelligentization and Remanufacture in Service of Equipment in Process Industry

Gao Jinji and Yang Guoan

Strategic Study of CAE 2015, Volume 17, Issue 7,   Pages 54-53

Abstract:

Process industries such as steel and petrochemical engineering are the pillar industry of national economy, which are the foundation to achieve “manufacturing prower” and the top 10 development areas of “Made in China 2025.” So far, the disharmony of equipment and process engineering in process industries results in the outstanding problems in safety and economic operation, such as high energy consumption and high failure rates of equipments. In this paper, the reasons are analyzed in design and construction, business management, government orientation and talent cultivation. Development strategies are put forward for greenization, intelligentization and remanufacture in service of equipments in process industries. Some strategies and policy proposals are also put forward for greening design and manufacture, intelligentized supervisory control of health and energy efficiency for equipment, and remanufacture in service.

Keywords： process industry     equipment     greenization     intelligentization     remanufacture in service    

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Smart Manufacturing and Intelligent Manufacturing: A Comparative Review Review

Baicun Wang, Fei Tao, Xudong Fang, Chao Liu, Yufei Liu, Theodor Freiheit

Engineering 2021, Volume 7, Issue 6,   Pages 738-757 doi: 10.1016/j.eng.2020.07.017

Abstract: provided, which is increasingly important because the trend to merge both terminologies rises in Industry 4.0

Keywords： Smart manufacturing     Intelligent manufacturing     Industry 4.0     Human–cyber–physical system (HCPS)    

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A novel model for assessing the degree of intelligent manufacturing readiness in the process industry: process-industry intelligent manufacturing readiness index (PIMRI) Research Article

Lujun ZHAO, Jiaming SHAO, Yuqi QI, Jian CHU, Yiping FENG

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 3,   Pages 417-432 doi: 10.1631/FITEE.2200080

Abstract: Recently, the implementation of has become a new tendency, and it brings both opportunities and challenges to worldwide manufacturing companies. Thus, many manufacturing companies are attempting to find advanced technologies to launch transformation. In this study, we propose a new model to measure the readiness for the , which aims to guide companies in recognizing their current stage and short slabs when carrying out transformation. Although some models have already been reported to measure readiness and maturity, there are no models that are aimed at the . This newly proposed model has six levels to describe different development stages for . In addition, the model consists of four races, nine species, and 25 domains that are relevant to the essential businesses of companies’ daily operation and capability requirements of . Furthermore, these 25 domains are divided into 249 characteristic items to evaluate the manufacturing readiness in detail. A questionnaire is also designed based on the proposed model to help process-industry companies easily carry out self-diagnosis. Using the new method, a case including 196 real-world process-industry companies is evaluated to introduce the method of how to use the proposed model. Overall, the proposed model provides a new way to assess the degree of readiness for process-industry companies.

Keywords： Process industry     Industry 4.0     Readiness model     Intelligent manufacturing     Readiness index    

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Smart Society and Artificial Intelligence: Big Data Scheduling and the Global Standard Method Applied to Smart Maintenance Article

Ruben Foresti, Stefano Rossi, Matteo Magnani, Corrado Guarino Lo Bianco, Nicola Delmonte

Engineering 2020, Volume 6, Issue 7,   Pages 835-846 doi: 10.1016/j.eng.2019.11.014

Abstract:

The implementation of artificial intelligence (AI) in a smart society, in which the analysis of human habits is mandatory, requires automated data scheduling and analysis using smart applications, a smart infrastructure, smart systems, and a smart network. In this context, which is characterized by a large gap between training and operative processes, a dedicated method is required to manage and extract the massive amount of data and the related information mining. The method presented in this work aims to reduce this gap with near-zero-failure advanced diagnostics (AD) for smart management, which is exploitable in any context of Society 5.0, thus reducing the risk factors at all management levels and ensuring quality and sustainability. We have also developed innovative applications for a humancentered management system to support scheduling in the maintenance of operative processes, for reducing training costs, for improving production yield, and for creating a human–machine cyberspace for smart infrastructure design. The results obtained in 12 international companies demonstrate a possible global standardization of operative processes, leading to the design of a near-zero-failure intelligent system that is able to learn and upgrade itself. Our new method provides guidance for selecting the new generation of intelligent manufacturing and smart systems in order to optimize human–machine interactions, with the related smart maintenance and education.

Keywords： Human-centered management system     Big data scheduling     Global standard method     Society 5.0     Industry 4.0    

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Fundamental Theories and Key Technologies for Smart and Optimal Manufacturing in the Process Industry

Feng Qian,Weimin Zhong,Wenli Du

Engineering 2017, Volume 3, Issue 2,   Pages 154-160 doi: 10.1016/J.ENG.2017.02.011

Abstract:

Given the significant requirements for transforming and promoting the process industry, we present the major limitations of current petrochemical enterprises, including limitations in decision-making, production operation, efficiency and security, information integration, and so forth. To promote a vision of the process industry with efficient, green, and smart production, modern information technology should be utilized throughout the entire optimization process for production, management, and marketing. To focus on smart equipment in manufacturing processes, as well as on the adaptive intelligent optimization of the manufacturing process, operating mode, and supply chain management, we put forward several key scientific problems in engineering in a demand-driven and application-oriented manner, namely: ① intelligent sensing and integration of all process information, including production and management information; ② collaborative decision-making in the supply chain, industry chain, and value chain, driven by knowledge; ③ cooperative control and optimization of plant-wide production processes via human-cyber-physical interaction; and ④ life-cycle assessments for safety and environmental footprint monitoring, in addition to tracing analysis and risk control. In order to solve these limitations and core scientific problems, we further present fundamental theories and key technologies for smart and optimal manufacturing in the process industry. Although this paper discusses the process industry in China, the conclusions in this paper can be extended to the process industry around the world.

Keywords： Process industry     Smart and optimal manufacturing     Green manufacturing     High-end manufacturing     Optimality assessment    

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Smart Process Manufacturing Systems: Deep Integration of Artificial Intelligence and Process Manufacturing

Feng Qian

Engineering 2019, Volume 5, Issue 6,   Pages 981-981 doi: 10.1016/j.eng.2019.10.002

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Study on “Industrial Internet” and “Industrie 4.0”

Yan Jianlin, and Kong Dejing

Strategic Study of CAE 2015, Volume 17, Issue 7,   Pages 141-140

Abstract: “Industrial Internet” from the U.S. and “Industrie 4.0” from Germany should be

Keywords： industrial internet     Industrie 4.0     mode transformation     “Made in China 2025”    

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The Future of Manufacturing: A New Perspective Perspective

Ben Wang

Engineering 2018, Volume 4, Issue 5,   Pages 722-728 doi: 10.1016/j.eng.2018.07.020

Abstract: focus on hardware, software, additive manufacturing, robotics, the Internet of Things, and Industry 4.0

Keywords： Advanced Manufacturing Partnership     Ecosystem     Industry 4.0     Intelligent manufacturing     Internet of Things    

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Development Strategy for Intelligent Factory in Discrete Manufacturing

Lu Bingheng, Shao Xinyu , Zhang Jun, Wang Lei

Strategic Study of CAE 2018, Volume 20, Issue 4,   Pages 44-50 doi: 10.15302/J-SSCAE-2018.04.008

Abstract:

A new-generation intelligent manufacturing, characterized by the marriage of the next-generation artificial intelligence technology and advanced manufacturing industry, is emerging and becoming the core technology of the fourth industrial revolution. China’s manufacturing industry as a whole is big but not strong. The development of intelligent manufacturing can promote China’s manufacturing industry by improving both quality and efficiency, and ultimately, is the main path for the transformation and upgrading of China’s manufacturing industry. Intelligent production is a major component of intelligent manufacturing, while intelligent factory is the carrier for intelligent production. This paper focuses on the development strategy for smart factories in discrete manufacturing. First, the concept of intelligent factory is introduced, with its basic structure, information system architecture, and basic characteristics in discrete manufacturing discussed. Then, the key breakthrough directions and the implementation plan for intelligent factory are laid out. Finally, following suggestions are provided for policy makers to develop intelligent factory: ① actively support and guide the development of intelligent manufacturing through pilot and demonstration projects, and promote the formation of an ecological chain for intelligent manufacturing with regional advantages; ② encourage enterprises to build intelligent factories to construct technological competitive advantages and enhance economic benefits; ③ establish and improve the synergy mechanism for innovation; ④ highlight the Made-in-China capabilities for core technologies, key equipment components, and industrial software.

Keywords： intelligent manufacturing     intelligent factory     discrete manufacturing    

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Learning deep IA bidirectional intelligence Personal View

Lei XU

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 4,   Pages 558-562 doi: 10.1631/FITEE.1900541

Abstract: There has been a framework sketched for learning deep bidirectional intelligence. The framework has an inbound that features two actions: one is the acquiring action, which gets inputs in appropriate patterns, and the other is A-S cognition, derived from the abbreviated form of words abstraction and self-organization, which abstracts input patterns into concepts that are labeled and understood by self-organizing parts involved in the concept into structural hierarchies. The top inner domain accommodates relations and a priori knowledge with the help of the A-I thinking action that is responsible for the accumulation-amalgamation and induction-inspiration. The framework also has an outbound that comes with two actions. One is called I-S reasoning, which makes inference and synthesis (I-S) and is responsible for performing various tasks including image thinking and problem solving, and the other is called the interacting action, which controls, communicates with, and inspects the environment. Based on this framework, we further discuss the possibilities of design intelligence through synthesis reasoning.

Keywords： Abstraction     Least mean square error reconstruction (Lmser)     Cognition     Image thinking     Abstract thinking     Synthesis reasoning    

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Understanding of a New Generation of Intelligent Manufacturing based on RAMI 4.0

Zhao Min

Strategic Study of CAE 2018, Volume 20, Issue 4,   Pages 90-96 doi: 10.15302/J-SSCAE-2018.04.015

Abstract: method of deconstruction and reconstruction, to analyze the Reference Architecture Model of Industrial 4.0(RAMI 4.0) of Germany, and compared with the general architecture of intelligent manufacturing proposed

Keywords： RAMI 4.0     cyber-physics system (CPS)     basic paradigm     new-generation artificial intelligence     architecture    

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Title Author Date Type Operation

Framework and case study of cognitive maintenance in Industry 4.0

Bao-rui Li, Yi Wang, Guo-hong Dai, Ke-sheng Wang,kesheng.wang@ntnu.no

Journal Article

Technological and Economical Logic for Intelligent Manufacturing

Guo Zhaohui

Journal Article

Intelligent Manufacturing in the Context of Industry 4.0: A Review

Ray Y. Zhong, Xun Xu, Eberhard Klotz, Stephen T. Newman

Journal Article

Opportunities and Challenges of Artificial Intelligence for Green Manufacturing in the Process Industry

Shuai Mao, Bing Wang, Yang Tang, Feng Qian

Journal Article

Greenization, Intelligentization and Remanufacture in Service of Equipment in Process Industry

Gao Jinji and Yang Guoan

Journal Article

Smart Manufacturing and Intelligent Manufacturing: A Comparative Review

Baicun Wang, Fei Tao, Xudong Fang, Chao Liu, Yufei Liu, Theodor Freiheit

Journal Article

A novel model for assessing the degree of intelligent manufacturing readiness in the process industry: process-industry intelligent manufacturing readiness index (PIMRI)

Lujun ZHAO, Jiaming SHAO, Yuqi QI, Jian CHU, Yiping FENG

Journal Article

Smart Society and Artificial Intelligence: Big Data Scheduling and the Global Standard Method Applied to Smart Maintenance

Ruben Foresti, Stefano Rossi, Matteo Magnani, Corrado Guarino Lo Bianco, Nicola Delmonte

Journal Article

Fundamental Theories and Key Technologies for Smart and Optimal Manufacturing in the Process Industry

Feng Qian,Weimin Zhong,Wenli Du

Journal Article

Smart Process Manufacturing Systems: Deep Integration of Artificial Intelligence and Process Manufacturing

Feng Qian

Journal Article

Study on “Industrial Internet” and “Industrie 4.0”

Yan Jianlin, and Kong Dejing

Journal Article

The Future of Manufacturing: A New Perspective

Ben Wang

Journal Article

Development Strategy for Intelligent Factory in Discrete Manufacturing

Lu Bingheng, Shao Xinyu , Zhang Jun, Wang Lei

Journal Article

Learning deep IA bidirectional intelligence

Lei XU

Journal Article

Understanding of a New Generation of Intelligent Manufacturing based on RAMI 4.0

Zhao Min

Journal Article