Metrics for the evaluation of data quality of signal data in industrial processes

Kirchen, Iris; Schütz, Daniel; Folmer, Jens; Vogel‐Heuser, Birgit

doi:10.1109/indin.2017.8104878

Cited by 19 publications

(15 citation statements)

References 12 publications

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“…Visualization, but it was also used for Predictive Maintenance (Mozgova et al 2018), to Detect Anomalies in Production (Zheng & Wu 2017) and to Improve Product Quality (Kirchen et al 2017). Clustering is a more advanced technique compared to Statistical Data analysis, and is used to find Production Anomalities (Y.…”

Section: Discussionmentioning

confidence: 99%

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Business analytics in Industry 4.0: A systematic review

Silva

Cortez

Pereira³

et al. 2021

Expert Systems

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Recently, the term "Industry 4.0" has emerged to characterize several Information Technology and Communication (ICT) adoptions in production processes (e.g., Internet-of-Things, implementation of digital production support information technologies). Business Analytics is often used within the Industry 4.0, thus incorporating its data intelligence (e.g., statistical analysis, predictive modeling, optimization) expert system component. In this paper, we perform a Systematic Literature Review (SLR) on this Business Analytics usage, covering a selection of 169 papers obtained from six major scientific publication sources from 2010 to March 2020. The selected papers were first classified in three major types, namely, Practical Application, Reviews and Framework Proposal. Then, we analyzed with more detail the practical application studies which were further divided into three main categories of the Gartner analytical maturity model, Descriptive Analytics, Predictive Analytics and Prescriptive Analytics. In particular, we characterized the distinct analytics studies in terms of the industry application and data context used, impact (in terms of their Technology Readiness Level) and selected data modeling method. Our SLR analysis provides a mapping of how data-based Industry 4.0 expert systems are currently used, disclosing also research gaps and future research opportunities.

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Section: Discussionmentioning

confidence: 99%

“…. Some studies use data warehousing databases and dashboards(Kirchen, Schütz, Folmer, & Vogel-Heuser 2017;Neuböck & Schrefl 2015;Vathoopan, Johny, Zoitl, & Knoll 2018;Zheng & Wu 2017), and other studies used Neural Networks(Kaupp, Beez, Hülsmann, & Humm 2019;C.-J. Kuo, Ting, Chen, Yang, & Chen 2017;Qin, Liu, & Grosvenor 2017;Subakti & Jiang 2018;Tieng et al 2018).…”

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Business analytics in Industry 4.0: A systematic review

Silva

Cortez

Pereira³

et al. 2021

Expert Systems

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“…Além disso, a análise de Big Data é um assunto que aparece como ponto importante dentro deste meio de tecnologias conectadas. Isto proporcionará cada vez mais dados em quantidades sem precedentes que precisam ser analisados KIRCHEN et al, 2017). Além disso, de um ponto de vista prático, este estudo auxilia gerentes industrias no enfrentamento dos desafios que podem surgir no contexto da Indústria 4.0 nos mais diferentes pontos de vista.…”

Section: Considerações Finaisunclassified

“…; Luthra e Mangla (2017);Kirchen et al (2017);Yan et al (2017);Xu et al (2018); Ur Rehman (2018);Chen et al (2018).1 -PMEs: Pequenas e Médias Empresas; 2-AIs: Inteligências Artificiais.Fonte: Soltoviski et al (2019) e-ISSN: 2446-6875 p-ISSN: 1807-5436 Gestão e Desenvolvimento | Novo Hamburgo | v. 17 | n. 3 | set./dez. 2020 A forma como os riscos foram mapeados por Soltovski et al (2019), também é base utilizada nesta pesquisa para entender a representativdidade dos riscos na literatura.3 PROCESSO DE REVISÃO SISTEMÁTICA DA LITERATURADentro do ramo da pesquisa, a grande quantidade de periódicos e publicações cientificas torna o trabalho dos pesquisadores algo complexo e extenso em relação ao material bibliográfico que irão utilizar.Neste aspecto, estabelecer critérios adequados para eleger sistematicamente os estudos mais relevantes para uma pesquisa pode auxiliar neste trabalho(PAGANI et al, 2015).…”

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Um Estudo Quantitativo Sobre Os Riscos Da Indústria 4.0 No Contexto Industrial: Uma Revisão Sistemática Da Literatura

Soltovski

Resende

Pontes

et al. 2020

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RESUMOA Indústria 4.0 é um tema em ascensão tanto no meio acadêmico como no contexto prático de empresas. As pesquisas atuais focam seus esforços nos estudos dos benefícios dos conceitos e tecnologias envolvidos no conceito da Quarta Revolução Industrial, no entanto, poucas procuram entender quais os pontos negativos. Dessa forma, através de uma revisão bibliográfica sistematizada realizada em janeiro de 2019, esse estudo procura entender quais são riscos em evidência na literatura que empresas do setor industrial podem enfrentar no contexto da Indústria 4.0. Foram selecionados 66 artigos através das bases de dados Scopus e Web of Science, os quais foram bibliometricamente estudados em relação aos autores e periódicos mais relevantes. Em seguida, estudando os 28 riscos mapeados na literatura por Soltovski et al. (2019), foi possível entender quais são mais representativos na literatura na perspectiva da sustentabilidade (ambiental, econômico e social) e na perspectiva tecnológica. De forma geral, descobrimos que os riscos relacionados à conectividade, como cyber atacks e vazamento de dados privados através de tecnologias, como Internet das Coisas, são os mais discutidos na literatura. Além disso, constatamos que existe carência de estudos na perspectiva ambiental e econômica. Por fim, foram sumarizadas contribuições gerenciais e potenciais direcionamentos para futuros pesquisadores.Palavras-chave: Indústria 4.0. Sustentabilidade. Gestão de Risco. ABSTRACTIndustry 4.0 is a growing topic, both in academia and in the practical context of companies. Current research focuses its efforts on studies of benefits and technologies involved in the concept of the Fourth Industrial Revolution, however, they rarely understand what the negative points are. Thus, through a systematic literature review conducted in January 2019, this study can understand what are the risks in evidence in the literature that companies in the industrial sector may face in the context of Industry 4.0. 66 articles were selected on the Scopus and Web of Science databases, which are the bibliometrically studied in relation to the most relevant authors and journals. Then, studying the 28 risks mapped in the literature by Soltovski et al. (2019), it was possible to understand which are the most representative in the literature from the perspective of sustainability (environmental, economic and social) and from a technological perspective. In general, we found that risks related to connectivity such as cyber attacks and data leakage are using technologies like the Internet of Things that are most discussed in the literature. In addition, we note that there is a lack of studies from an environmental and economic perspective. Finally, managerial contributions were summarized and directed to future researchers.Keywords: Industry 4.0. Sustainability. Risk Management.

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“…A major concern during data analysis in IMPROVE is data quality [23]. As common pre-treatment operations have to be carried out on the data, centralizing this functionality in a library minimizes overhead and the replication of code.…”

Section: Improve: Data Analytics and Securitymentioning

confidence: 99%

System architectures for Industrie 4.0 applications

Trunzer

Calà

Leitão

et al. 2019

Prod. Eng. Res. Devel.

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Industrie 4.0 principles demand increasing flexibility and modularity for automated production systems. Current system architectures provide an isolated view of specific applications and use cases, but lack a global, more generic approach. Based on the specific architectures of two EU projects and one German Industrie 4.0 project, a generic system architecture is proposed. This system architecture features the strengths of the three isolated proposals, such as cross-enterprise data sharing, service orchestration, and real-time capabilities, and can be applied to a wide field of applications. Future research should be directed towards considering the applicability of the architecture to other equal applications.

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Metrics for the evaluation of data quality of signal data in industrial processes

Cited by 19 publications

References 12 publications

Business analytics in Industry 4.0: A systematic review

Business analytics in Industry 4.0: A systematic review

Um Estudo Quantitativo Sobre Os Riscos Da Indústria 4.0 No Contexto Industrial: Uma Revisão Sistemática Da Literatura

System architectures for Industrie 4.0 applications

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