A big data driven analytical framework for energy-intensive manufacturing industries

Zhang, Yingfeng; Ma, Shuaiyin; Yang, Haidong; Lv, Jingxiang; Liu, Yang

doi:10.1016/j.jclepro.2018.06.170

Cited by 187 publications

(75 citation statements)

References 68 publications

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“…'Big data' is used to refer to the speedy capturing (high velocity collection) of data that consists of large volumes of varyingly complex datasets [17] [18]. The term 'big data' has become relevant in recent times due to the increased volume of data generation in industry.…”

Section: Identifying Data Availability Through Big Data Enablement (Qmentioning

confidence: 99%

“…Various studies concerning big data platforms [18]- [23] were evaluated. From these studies it was found that measurement instrumentation installed on equipment forms the initial stage in the data pipeline.…”

Section: Identifying Data Availability Through Big Data Enablement (Qmentioning

confidence: 99%

“…The available data consist of sample data, continuous real-time data, and long-term data. These generic types of data availability identified from the literature [18]- [23] are used as the levels of data that can be used in reporting applications.…”

Section: Identifying Data Availability Through Big Data Enablement (Qmentioning

confidence: 99%

See 2 more Smart Citations

Finding the Four Qualities of Intelligent Industrial Reporting

Botes

Hamer

Jaarsveld

et al. 2019

SAJIE

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Internationally, innovation and technology are driving change through concepts such as 'Industry 4.0'. However, due to various constraints, South Africa is lagging behind in this transformation. Furthermore, local industry generates large amounts of data that could contribute to a positive transformation. Data analytics, in the form of reporting, may therefore present a workable alternative to understand better the intricate nature of real-world operations. This paper identifies four qualities for the practical application of data analytics, with the aim of intelligent reporting. The four qualities are focus area, data availability, analytics, and visualisation. Research on each quality shows that they have various levels. A comprehensive literature review supports these findings. Forty studies are included that were selected through a process of relevant research criteria. A case study is presented to show how the four qualities contribute to the development of intelligent reports as an objective representation of industry performance.

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Section: Identifying Data Availability Through Big Data Enablement (Qmentioning

confidence: 99%

Section: Identifying Data Availability Through Big Data Enablement (Qmentioning

confidence: 99%

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Finding the Four Qualities of Intelligent Industrial Reporting

Botes

Hamer

Jaarsveld

et al. 2019

SAJIE

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“…During the last few years, wind, solar, hydro and nuclear power companies have greatly benefited from the power of AI, big data, machine learning and predictive models. They used these technologies to make better predictions, to increase their portfolio's rate of return and to lower their costs [2].…”

Section: Introductionmentioning

confidence: 99%

A survey of big data and machine learning

Salkuti¹

2020

IJECE

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This paper presents a detailed analysis of big data and machine learning (ML) in the electrical power and energy sector. Big data analytics for smart energy operations, applications, impact, measurement and control, and challenges are presented in this paper. Big data and machine learning approaches need to be applied after analyzing the power system problem carefully. Determining the match between the strengths of big data and machine learning for solving the power system problem is of utmost important. They can be of great help to plan and operate the traditional grid/smart grid (SG). The basics of big data and machine learning are described in detailed manner along with their applications in various fields such as electrical power and energy, health care and life sciences, government, telecommunications, web and digital media, retailers, finance, e-commerce and customer service, etc. Finally, the challenges and opportunities of big data and machine learning are presented in this paper.

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“…Recently, the rapid progress of information technology (e.g. radio frequency identification-RFID) provides shop floor with rich real-time data for better operational management [26] [27]. With these technologies being adopted, real-time-data-based traceability, visibility, and interoperability can be realized to improve the performance of shop floor planning, monitoring, and control.…”

Section: Introductionmentioning

confidence: 99%

Multiagent and Bargaining-Game-Based Real-Time Scheduling for Internet of Things-Enabled Flexible Job Shop

Wang

Zhang

Liu

et al. 2019

IEEE Internet Things J.

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With the rapid advancement and widespread applications of information technology in the manufacturing shop floor, a huge amount of real-time data is generated, providing a good opportunity to effectively respond to unpredictable exceptions so that the productivity can be improved. Thus, how to schedule the manufacturing shop floor for achieving such a goal is very challenging. This work addresses this issue and a new multi-agent-based real-time scheduling (MARS) architecture is proposed for an Internet of Things (IoT)-enabled flexible job shop. Differing from traditional dynamic scheduling strategies, the proposed strategy optimally assigns tasks to machines according to their real-time status. A bargaining-game-based negotiation mechanism is developed to coordinate the agents so that the problem can be efficiently solved. To demonstrate the feasibility and effectiveness of the proposed architecture and scheduling method, a proof-of-concept prototype system is implemented with Java agent development framework (JADE) platform. A case study is used to test the performance and effectiveness of the proposed method. Through simulation and comparison, it is shown that the proposed method outperforms the traditional dynamic scheduling strategies in terms of makespan, critical machine workload, and total energy consumption. be obtained from the IEEE by sending a request to pubs-permissions@ieee.org. Recently, investigations have been made by a number of scholars on multi-agent-based dynamic scheduling [22][23]. However, most of these researches mainly focus on the architectures of multi-agent systems (MAS) and negotiation protocols among the agents, as well as the application of distributed features of MAS for task allocation in a traditional manufacturing shop floor [24]. Few of them consider the real-time-data-based interaction between machines and other distributed resources in an IoT-enabled flexible job shop. As a result, often the performance of efficiency is degraded and

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A big data driven analytical framework for energy-intensive manufacturing industries

Cited by 187 publications

References 68 publications

Finding the Four Qualities of Intelligent Industrial Reporting

Finding the Four Qualities of Intelligent Industrial Reporting

A survey of big data and machine learning

Multiagent and Bargaining-Game-Based Real-Time Scheduling for Internet of Things-Enabled Flexible Job Shop

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