Industrial Big Data in an Industry 4.0 Environment: Challenges, Schemes, and Applications for Predictive Maintenance

Yan, Jihong; Meng, Yue; Lu, Lihua; Lin, Li

doi:10.1109/access.2017.2765544

Cited by 362 publications

(201 citation statements)

References 17 publications

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“…In SF system, big data are collected about the resources, and products, and analyzed in real time to forecast equipment status and information. Patterns can be drawn and a suitable maintenance strategy can be tested and evaluated [101]. Existing works in predictive maintenance can be classified to the following:…”

Section: Predictive Maintenancementioning

confidence: 99%

“…Sayed et al [111] suggested a framework based on inferencing probability of the occurrence of an unobservable fault hypothesis based on the measured (observed) evidence. Yan et al [101] suggested a framework for structuring and characterizing multisource industrial big data using semantic web technology. The framework enhanced by enveloped analysis and fusion method to identify data patterns.…”

Section: Predictive Maintenancementioning

confidence: 99%

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Requirements of the Smart Factory System: A Survey and Perspective

et al. 2018

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Abstract:With the development of Industry 4.0 and the emergence of the smart factory concept, the traditional philosophy of manufacturing systems will change. The smart factory introduces changes to the factors and elements of traditional manufacturing systems and incorporates the current requirements of smart systems so that it can compete in the future. An increasing amount of research in both academia and industry is dedicated to transitioning the concept of the smart factory from theory to practice. The purpose of the current research is to highlight the perspectives that shape the smart factory and to suggest approaches and technical support to enable the realization of those perspectives. This paper fills this gap by identifying and analyzing research on smart factories.We suggest a framework to analyze existing research and investigate the elements and features of smart factory systems.

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Section: Predictive Maintenancementioning

confidence: 99%

Section: Predictive Maintenancementioning

confidence: 99%

Requirements of the Smart Factory System: A Survey and Perspective

et al. 2018

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“…Hence, KBM may contribute to the enhancement of business values by manufacturing companies, based on decreasing maintenance costs and most importantly retaining and increasing availability of facilities over time. Focusing on big data in the literature of maintenance management, Yan et al (2017) addressed the challenge for structuring multisource heterogeneous information for predictive maintenance and proposed a framework for characterising structured data with multi-scale analysis (Yan et al 2017). The proposed multi-scale analysis takes into consideration the spatio-temporal properties (i.e.…”

Section: Review Of Related Kbm Approachesmentioning

confidence: 99%

“…system-dependent and time-dependent) and modelling invisible factors (i.e. hidden root-causes and cause-effect interrelations) for causality mining (Yan et al 2017). Zhang et al (2017) also provided a big data analytics architecture for maintenance processes of complex products, which deals with structuring multi-source heterogeneous data ).…”

Section: Review Of Related Kbm Approachesmentioning

confidence: 99%

PriMa: a prescriptive maintenance model for cyber-physical production systems

Ansari

Grepl

Nemeth

2019

International Journal of Computer Integrated Manufacturing

142

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Cyber-physical production systems (CPPS), as an emerging Industry 4.0's technology, trigger a paradigm shift from descriptive to prescriptive maintenance. In particular, maintenance management approaches nowadays are more and more transformed to (semi-) automated knowledge-based decision support systems. This paper is intended to examine existing approaches and challenges towards rethinking maintenance in the context of Industry 4.0 and thus contributes to the literature of production management and planning, by introducing a novel prescriptive maintenance model (PriMa). PriMa is comprising of four layers (i.e. data management, predictive data analytic toolbox, recommender and decision support dashboard as well as an overarching layer for semantic-based learning and reasoning). The integrated approach of PriMa enhances two functional capabilities, namely i) efficiently processing large amount of multi-modal and heterogeneous data collected from multidimensional data sources and ii) effectively generating decision support measures and recommendations for improving and optimising forthcoming maintenance plans correlated with production planning and control (PPC) systems. An industry-oriented proof-of-concept study has been conducted to explore the feasibility of applying PriMa in real production systems by implementing a decision support solution and achieving a significant reduction of downtime. Finally, future research directions in this area are outlined. ARTICLE HISTORY

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“…Besides the objective parameters related to the product quality, mainly regarding defect tracking, product monitoring also refers to the subjective assessment of their quality. To this end, customer feedback and market analysis are required [38,39]. Thanks to modern smart devices, these activities are performed in an easier, more efficient and cost-effective way: feedback will be gathered using the mobile crowdsourcing paradigm, data coming from customers will be integrated with market-related data and, based on them, advanced analysis techniques will be used to generate relevant KPIs (Key Performance Indicators).…”

Section: Product Monitoringmentioning

confidence: 99%

How Data Will Transform Industrial Processes: Crowdsensing, Crowdsourcing and Big Data as Pillars of Industry 4.0

Pilloni

2018

Future Internet

120

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We are living in the era of the fourth industrial revolution, namely Industry 4.0. This paper presents the main aspects related to Industry 4.0, the technologies that will enable this revolution, and the main application domains that will be affected by it. The effects that the introduction of Internet of Things (IoT), Cyber-Physical Systems (CPS), crowdsensing, crowdsourcing, cloud computing and big data will have on industrial processes will be discussed. The main objectives will be represented by improvements in: production efficiency, quality and cost-effectiveness; workplace health and safety, as well as quality of working conditions; products' quality and availability, according to mass customisation requirements. The paper will further discuss the common denominator of these enhancements, i.e., data collection and analysis. As data and information will be crucial for Industry 4.0, crowdsensing and crowdsourcing will introduce new advantages and challenges, which will make most of the industrial processes easier with respect to traditional technologies.

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Industrial Big Data in an Industry 4.0 Environment: Challenges, Schemes, and Applications for Predictive Maintenance

Cited by 362 publications

References 17 publications

Requirements of the Smart Factory System: A Survey and Perspective

Requirements of the Smart Factory System: A Survey and Perspective

PriMa: a prescriptive maintenance model for cyber-physical production systems

How Data Will Transform Industrial Processes: Crowdsensing, Crowdsourcing and Big Data as Pillars of Industry 4.0

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