scholarly journals Research on the new mode of intelligent maintenance and health management of coal mine equipment

2021 ◽  
Author(s):  
Xiangang Cao ◽  
Mengyuan Zhang ◽  
Yong Duan ◽  
Kexin Wu ◽  
Yanchuan Li
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Life Cycle ◽  
Coal Mine ◽  
System Architecture ◽  
Reference Model ◽  
Health Management ◽  
Subsequent Development ◽  
Intelligent Maintenance ◽  
Key Technologies

Abstract Based on the analysis of the current challenges and deficiencies in the maintenance and management of coal mine equipment, an intelligent maintenance and health management system framework for coal mine equipment is designed for the big data characteristics of the life cycle of coal mine equipment. Taking the big data processing and analysis of coal mine equipment as the main line, it proposes and elaborates the key technologies of the intelligent maintenance and health management of coal mine equipment driven by big data, including the unified description and analysis of multi-source heterogeneous big data, and intelligent fault diagnosis of coal mine equipment. Technology, health evaluation and prediction technology, intelligent maintenance decision-making technology, etc. Through the implementation of the above-mentioned system architecture and key technologies, data-driven life-cycle intelligent decision-making is realized, which promotes the continuous optimization and improvement of equipment process management and reduces business costs. The proposed system architecture provides a reference model for subsequent development.

Information Visualization and Policy Modeling

2014 ◽  
pp. 175-215
Author(s):  
Kawa Nazemi ◽  
Martin Steiger ◽  
Dirk Burkhardt ◽  
Jörn Kohlhammer
Keyword(s):  
Big Data ◽  
Life Cycle ◽  
Information Visualization ◽  
Policy Design ◽  
Reference Model ◽  
Main Idea ◽  
Human Interaction ◽  
The Political ◽  
Policy Modeling ◽  
The Right

Policy design requires the investigation of various data in several design steps for making the right decisions, validating, or monitoring the political environment. The increasing amount of data is challenging for the stakeholders in this domain. One promising way to access the “big data” is by abstracted visual patterns and pictures, as proposed by information visualization. This chapter introduces the main idea of information visualization in policy modeling. First abstracted steps of policy design are introduced that enable the identification of information visualization in the entire policy life-cycle. Thereafter, the foundations of information visualization are introduced based on an established reference model. The authors aim to amplify the incorporation of information visualization in the entire policy design process. Therefore, the aspects of data and human interaction are introduced, too. The foundation leads to description of a conceptual design for social data visualization, and the aspect of semantics plays an important role.

Information Visualization and Policy Modeling

Big Data ◽  
2016 ◽  
pp. 139-180
Author(s):  
Kawa Nazemi ◽  
Martin Steiger ◽  
Dirk Burkhardt ◽  
Jörn Kohlhammer
Keyword(s):  
Big Data ◽  
Life Cycle ◽  
Information Visualization ◽  
Policy Design ◽  
Reference Model ◽  
Main Idea ◽  
Human Interaction ◽  
The Political ◽  
Policy Modeling ◽  
The Right

Policy design requires the investigation of various data in several design steps for making the right decisions, validating, or monitoring the political environment. The increasing amount of data is challenging for the stakeholders in this domain. One promising way to access the “big data” is by abstracted visual patterns and pictures, as proposed by information visualization. This chapter introduces the main idea of information visualization in policy modeling. First abstracted steps of policy design are introduced that enable the identification of information visualization in the entire policy life-cycle. Thereafter, the foundations of information visualization are introduced based on an established reference model. The authors aim to amplify the incorporation of information visualization in the entire policy design process. Therefore, the aspects of data and human interaction are introduced, too. The foundation leads to description of a conceptual design for social data visualization, and the aspect of semantics plays an important role.

A Big Data Framework for Decision Making in Supply Chain

2019 ◽  
pp. 1-22 ◽  
Author(s):  
Kamalendu Pal
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Supply Chain ◽  
Information And Communication Technologies ◽  
Data Storage ◽  
Reference Model ◽  
Handheld Devices ◽  
Operational Environment ◽  
Scor Model ◽  
Mathematical Techniques

The advent of information and communication technologies (ICT) ushers a cost-effective prospect to take care of large volumes of complex data, commonly known as “big data” in the supply chain operational environment. Big data is being generated today by web applications, social media, intelligent machines, sensors, mobile phones, and other smart handheld devices. Big data is characterized in terms of the velocity, volume, and variety with which it produces along the supply chain. This is due to recent advances in telecommunication networks along with centralized and decentralized data storage systems, which are processed thanks to modern digital computational capabilities. There is a growing interest in the use of this large volume of data and advanced analytics for diverse types of business problems in supply chain management (SCM). Such decision-support software applications employ pure mathematical techniques, artificial intelligence techniques, and sometimes uses both techniques to perform analytical operations that undercover relationships and patterns within supply chain generated big data. This chapter proposes a framework for the utilization of big data in SCM decision making. The framework is based on the SCOR (supply chain operations reference) model, which is endorsed by Supply Chain Council (SCC). The proposed framework is influenced by the enterprise potential of augmented reality and virtual reality in supply chain applications, and it identifies key categories of big data analytics applications for the key businesses of SCOR model. Finally, the chapter highlights research issues to extract insight from big data sources for enterprise decision making.

scholarly journals System Architecture and Key Technologies for the Whole Life Cycle of Smart Road

2021 ◽  
Vol 1972 (1) ◽  
pp. 012105
Author(s):  
Zhen Liu ◽  
Xingyu Gu ◽  
Yihan Chen ◽  
Yizheng Chen
Keyword(s):  
Life Cycle ◽  
System Architecture ◽  
Key Technologies ◽  
Whole Life Cycle

A Big Data Framework for Decision Making in Supply Chain

2021 ◽  
pp. 312-333
Author(s):  
Kamalendu Pal
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Supply Chain ◽  
Information And Communication Technologies ◽  
Data Storage ◽  
Reference Model ◽  
Handheld Devices ◽  
Operational Environment ◽  
Scor Model ◽  
Mathematical Techniques

The advent of information and communication technologies (ICT) ushers a cost-effective prospect to take care of large volumes of complex data, commonly known as “big data” in the supply chain operational environment. Big data is being generated today by web applications, social media, intelligent machines, sensors, mobile phones, and other smart handheld devices. Big data is characterized in terms of the velocity, volume, and variety with which it produces along the supply chain. This is due to recent advances in telecommunication networks along with centralized and decentralized data storage systems, which are processed thanks to modern digital computational capabilities. There is a growing interest in the use of this large volume of data and advanced analytics for diverse types of business problems in supply chain management (SCM). Such decision-support software applications employ pure mathematical techniques, artificial intelligence techniques, and sometimes uses both techniques to perform analytical operations that undercover relationships and patterns within supply chain generated big data. This chapter proposes a framework for the utilization of big data in SCM decision making. The framework is based on the SCOR (supply chain operations reference) model, which is endorsed by Supply Chain Council (SCC). The proposed framework is influenced by the enterprise potential of augmented reality and virtual reality in supply chain applications, and it identifies key categories of big data analytics applications for the key businesses of SCOR model. Finally, the chapter highlights research issues to extract insight from big data sources for enterprise decision making.

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