Applications of Big Data analytics and Related Technologies in Maintenance—Literature-Based Research

Baum, Jens; Laroque, Christoph; Oeser, Benjamin; Skoogh, Anders; Subramaniyan, Mukund

doi:10.3390/machines6040054

Cited by 27 publications

(17 citation statements)

References 17 publications

(21 reference statements)

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“…6 and deserve special attention in the age of digitization. An excellent and broad coverage of the use of big data and related technologies in maintenance is given by Baum et al (2018). A more focused coverage on controllers is provided by Gao et al (2016).…”

Section: Process-diagnosis Modulementioning

confidence: 99%

A smart process controller framework for Industry 4.0 settings

Cohen

Singer

2021

J Intell Manuf

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This paper presents a smart supervisory framework for a single process controller, designed for Industry 4.0 shop floors. This digitization of a full supervisory suite for a single process controller enables self-awareness, self-diagnosis, self-prognosis, and self-healing (by definition, these "self" elements are missing from other supervisory frameworks diagnosing numerous controllers in parallel). The proposed framework is aligned with the concept of a Cyber Physical System (CPS), since its implementation generates a rich cyber physical entity of the controlled process. This CPS entity can either be considered as the process digital twin, or can provide a solid basis for generating it. Finally, the framework includes the main characteristics of Industry 4.0, such as advanced use of Artificial Intelligence (AI) and big data analysis. The framework is based on four modules: (1) Control and Awareness module-performing both continuous process control and adjustments, as well as machine learning (ML) and statistical process control (SPC) for identifying abnormalities that require further diagnosis; (2) Process-diagnosis module-performing continual (recurrent) analysis of the process state and trends; (3) Prognosis and Healing module-performing prognosis and automated intervention via parameter changes, re-configurations, and automated maintenance; (4) External Interaction Platform-an interactive module for interfacing with experts, presenting them with the process analysis information and obtaining feedback from them as part of a learning process. Using an implementation showcase to illustrate the methodological framework's applicability, we demonstrate its real-world potential. The proposed framework could serve as a guide for implementing smart process control and maintenance systems in Industry 4.0 shop floors. It could also provide a firm basis for comparison with future suggested frameworks. Future research directions could include pursuing improvements to the proposed process control framework and validating the framework by case studies of its implementation.

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Section: Process-diagnosis Modulementioning

confidence: 99%

A smart process controller framework for Industry 4.0 settings

Cohen

Singer

2021

J Intell Manuf

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“…On the other hand, in the technology perspective, the WWW leverages new technologies in the Industries and also evolves the corporate with infinite paths to attract the attention of the stakeholders. To attain this, Big data is evolved [1,2,5,8,21] with a capacity of high data storage in terms of Volume, the capability to process high speed generation of digital data in terms of Velocity and deal with unstructured, semi structured, quasi structured and structured data in terms of Variety. And characteristic of Veracity concentrates the importance of addressing the uncertainty in digital data.…”

Section: Figure 1 Web Mining Typesmentioning

confidence: 99%

“…The value of the web usage mining using traditional methodologies decrease over time, many applications in the present scenario require real time analysis of the transaction data that leads to unbiased conclusions. This situation demands the employment of techniques and technologies of big data analytics for analyzing web usage data effectively [2,5,8,22,23].…”

Section: Figure 4 Systematic Framework Of the Proposed Studymentioning

confidence: 99%

A Complete Research on Techniques & Technologies of Big Web Data Preparation to Web User Usage Behaviour

2019

IJRTE

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The rapid advancements in data digitization, the most powerful inventions of learning methodologies in data collection and reduced cost of data storage further enabled the World Wide Web with immense amount of data at significant rate in all the key domains. The generated web data is non-scalable, high dimensional, widely distributed, heterogeneous, dynamic in nature and having useful insights, and thus, it evolved as big data. This situation creates inevitably increasing opportunities in extracting structured solutions from unstructured weblog data for the present big data researchers. Moreover, to provide value addition to any key domain and derive actionable knowledge for various applications, such as, web usage analysis for improvements in fraud detection, product analysis and customer segmentation, got the focus in big data era by the web analysts. To improve operational performance and to discover hidden insights accurately, a comprehensive process is required to investigate the web user usage behavior by analyzing big web data. Towards this, the authors concentrate on reviewing the techniques and technologies of web data collection and preparation for investigating web user usage behavior effectively. In the present paper, the researchers initially pay an attention to explore web log data preparation methods in the traditional approach. Later, the review emphasizes on Hadoop approach for big data preparation and processing. This approach able to concentrate comprehensively on both the stages: distributed data storage and parallel processing of weblog data and to leverage the strengths of techniques and technologies of individual stages. Moreover, the authors deliberately review the possible potential research paths that results in an improved methodologies for data storage and optimized processing speed in the era of big web data

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“…Huge amount of data has been gathered from educational institutions [3] like from students, faculty, exam results, course details etc. Proper analysis of data should be done to improve the effectiveness and quality of the institution.…”

Section: Fig 6 Big Data In Educational Sectormentioning

confidence: 99%

Big Data Analytics and Its Applications

Memon

Soomro

Jumani

et al. 2019

IJRTE

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Big Data plays an important role in today’s environment. As technology is rapidly growing, massive amount of data is being generated from various sources like social media, business organizations, healthcare sector, government sectors, educational institutions, iot applications through sensors and many more. It’s a tremendous task to handle such large amount of data by using relational database management systems. This paper briefly describes about what are the various tools and techniques used to manage the data.

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Applications of Big Data analytics and Related Technologies in Maintenance—Literature-Based Research

Cited by 27 publications

References 17 publications

A smart process controller framework for Industry 4.0 settings

A smart process controller framework for Industry 4.0 settings

A Complete Research on Techniques & Technologies of Big Web Data Preparation to Web User Usage Behaviour

Big Data Analytics and Its Applications

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