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Industrial Big Data Analytics: Challenges, Methodologies, and Applications Junping Wang, Member, IEEE, Wensheng Zhang, Youkang Shi, Shihui Duan, Jin Liu

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Industrial Big Data Analytics: Challenges, Methodologies, and Applications Junping Wang, Member, IEEE, Wensheng Zhang, Youkang Shi, Shihui Duan, Jin Liu SUBMITTED TO IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 1 Industrial Big Data Analytics: Challenges, Methodologies, and Applications JunPing Wang, Member, IEEE, WenSheng Zhang, YouKang Shi, ShiHui Duan, Jin Liu Abstract—While manufacturers have been generating highly maintenance decisions which can avoid costly failures and distributed data from various systems, devices and applications, a unplanned downtime. However, the ability to perform analysis number of challenges in both data management and data analysis on the data is constrained by the increasingly distributed nature require new approaches to support the big data era. These of industrial data sets. Highly distributed data sources bring challenges for industrial big data analytics is real-time analysis about challenges in industrial data access, integration, and shar- and decision-making from massive heterogeneous data sources in ing. Furthermore, massive data produced by different sources manufacturing space. This survey presents new concepts, method- ologies, and applications scenarios of industrial big data analytics, are often defined using different representation methods and which can provide dramatic improvements in velocity and verac- structural specifications. Bringing those data together becomes ity problem solving. We focus on five important methodologies of a challenge because the data are not properly prepared for data industrial big data analytics: 1) Highly distributed industrial data integration and management, and the technical infrastructures ingestion: access and integrate to highly distributed data sources lack the appropriate information infrastructure services to from from various systems, devices and applications; 2) Industrial support big data analytics if it remains distributed. big data repository: cope with sampling biases and heterogeneity, Recently, industrial big data analytics has attracted extensive and store different data formats and structures; 3) Large-scale research interests from both academia and industry. According industrial data management: organizes massive heterogeneous to a report from McKinsey institute, the effective use of data and share large-scale data; 4) Industrial data analytics: track industrial big data has the underlying benefits to transform data provenance, from data generation through data preparation; 5) Industrial data governance: ensures data trust, integrity and economies, and delivering a new wave of productive growth. security. For each phase, we introduce to current research in Taking advantages of valuable industrial big data analytics will industries and academia, and discusses challenges and potential become basic competition for todays enterprises and will create solutions. We also examine the typical applications of industrial new competitors who are able to attract employees that have big data, including smart factory visibility, machine fleet, energy the critical skills on industrial big data [3]. The GE Corporate management, proactive maintenance, and just in time supply published while book about an industrial big data platform. chain. These discussions aim to understand the value of industrial It illustrates industrial big data requirements that must be big data. Lastly, this survey is concluded with a discussion of open addressed in order for industrial operators to achieve the many problems and future directions. efficient opportunities in a cost-effective manner. The industrial Keywords—Industry 4.0, cloud robotics, industrial big data, big data software platform brings these capabilities together predictive manufacturing. in a single technology infrastructure that opens the whole capabilities for service provider [4]. Brian corporate describes I. INTRODUCTION industrial big data analytics. The industrial big data analytics will focus on high-performance operational data management HEN manufacturers have been entered the age of big system, cloud-based data storage, and hybrid service platforms W data, Data sizes can range from a few dozen terabytes [5]. ABB corporate proposes that turn industrial big data into to many petabytes of data in a single data set. For example, the decision-making so that enterprise have additional context and GE company that produces a personal care product generates insight to enable better decision making [6]. In 2015, industrial 5,000 data samples every 33 milliseconds, resulting in [1]. Big big data analytics in [7] is proposed for manufacturing main- data analytics will be vital foundation for forecasting manu- tenance and service innovation, which discusses automate data facturing, machine fleet, and proactive maintenance. Compared processing, health assessment and prognostics in industrial big to big data in general, industrial big data has the potential to data environment. create value in different sections of manufacturing business This survey presents new reference model, discusses chain [2]. For example, valuable information regarding the methodologies challenges, potential solutions and application hidden degradation or inefficiency patterns within machines or development of industrial big data analytics. The main contri- manufacturing processes can lead to informed and effective butions are as follows: J. Wang and W. Zhang are with Laboratory of Precision Sensing and Control 1) From a systems-level view, we proposes new industrial Center, Institute of Automation, Chinese Academy, Beijing, China Email: big data analytics reference model for manufacturers, which [email protected],[email protected]. can real-time access, integrate, manage and analyse of massive S. Duan and Y. Shi are with Communications Standards Research Institute, heterogeneous data sources. The aim of this reference model is China Academy of Telecommunication Research of MIIT, Beijing, China Email: [email protected], [email protected]. to achieve massive predictive manufacturing on three different J. Liu is with State Key Laboratory of Software Engineering, Computer levels: massive heterogeneous data real-time acquisition and School, Wuhan, China Email:[email protected]. integration, massive industrial data management, intelligent SUBMITTED TO IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2 analytics and online decision-making. 3) Enterprise operation data: those data includes organi- 2) This survey discusses challenges, development and oppor- zation structure, business management, production, devices, tunities of five typical methodologies, which includes Highly marketing, quality control, production, procurement, inventory, distributed industrial data ingestion, large-scale data manage- goals, plans, e-commerce and other data. Enterprise operation ment techniques, industrial data analytics, industrial big data processes data will be innovation enterprise research and de- knowledge repository, and industrial big data governance. velopment, production, operation, marketing and management 3) This survey also discusses application development and style. First of all, production lines and devices data can be used value in four typical area, which include Smart Factory Visi- for real-time monitoring of equipment itself, at the same time bility, Machine Fleet, Energy Management, Proactive Mainte- production data generated by the feedback to the production nance, Just in Time Supply Chain, and Service Innovation. process, industrial control and management optimization. Sec- The remainder of the survey is organized as follows: Section ond, through the procurement, storage, sales, distribution and II will discuss the concept, opportunities and challenges of following procedures data collection and analysis of supply industrial big data analytics, and presents a reference model chain link, will lead to the efficiency of the improved and and the key challenges of each step in the model. Section III costs have fallen sharply, and will greatly reduce inventory, give typical technologies solutions, challenges and develop- improve and optimize the supply chain. Again, the use of the ment of industrial big data analytics to handle data-intensive change of the sales data, supplier data, can dynamically adjust applications in Section IV, where categorize the applications the rhythm of the optimal production, inventory and scale. In of industrial big data analytics into four groups, presenting addition, the energy management system based on real-time some application scenarios in each group. Section V finally perception, can realtime optimize of energy efficiency in the concludes the article. production process. 4) Manufacturing value chain: It includes customers, sup- II. THE DATA SOURCES,CHALLENGES AND pliers, partners and other data. To compete in the current DEVELOPMENT OF INDUSTRIAL BIG DATA ANALYTICS global economic environment, enterprises need to fully under- stand the technology development, production, procurement, A. Highly Distributed Data Sources sales, services, internal and external logistics competitiveness Industrial big data has been produced by diverse sources factors. Big data technology development and application of in manufacturing spaces, such as sensors, devices, logistics information flow of each link make up the value chain can vehicles, factory buildings, humans, tacking manufacturing be in-depth analysis and mining, for enterprise managers and process element(increased production efficiency, factory pol- participants see the new Angle of view of the value chain, lution, reduced energy consumption, and reduced cost of makes the enterprise have
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