Towards Predicting System Disruption in Industry 4.0: Machine Learning-Based Approach

Brik, Bouziane; Bettayeb, Belgacem; Sahnoun, M’hammed; Duval, Fabrice

doi:10.1016/j.procs.2019.04.089

Cited by 41 publications

(15 citation statements)

References 17 publications

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“…The work of [186,187] are especially interesting as they aim is to not only perform production management but also propose data for various health related analysis to create a safer working environment on the factory floor. A fog system for production management has been presented in [188] who use activity data to determine resource allocation locations to contribute to management of a production operation. Furthermore, product inspection, which is a common application of instrumentation systems in a factory, has been performed by [189,190] who utilize images and sensor data in a cloud based system to monitor product quality.…”

Section: Smart Industrymentioning

confidence: 99%

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

et al. 2021

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Internet of Things (IoT) is a system that integrates different devices and technologies, removing the necessity of human intervention. This enables the capacity of having smart (or smarter) cities around the world. By hosting different technologies and allowing interactions between them, the internet of things has spearheaded the development of smart city systems for sustainable living, increased comfort and productivity for citizens. The IoT for Smart Cities has many different domains and draws upon various underlying systems for its operation. In this paper, we provide a holistic coverage of the Internet of Things in Smart Cities. We start by discussing the fundamental components that make up the IoT based Smart City landscape followed by the technologies that enable these domains to exist in terms of architectures utilized, networking technologies used as well as the Artificial Algorithms deployed in IoT based Smart City systems. This is then followed up by a review of the most prevalent practices and applications in various Smart City domains. Lastly, the challenges that deployment of IoT systems for smart cities encounter along with mitigation measures.

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Section: Smart Industrymentioning

confidence: 99%

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

et al. 2021

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“…In particular, the most explored method was the Genetic Algorithm (Khayyam et al 2019;Silva, Jesus, Villaverde, & Adina 2020). Other authors (Ansari, Glawar, & Nemeth 2019;Brik, Bettayeb, Sahnoun, & Duval 2019;Fu, Ding, Wang, & Wang 2018;H. Li 2016;Qu, Wang, Govil, & Leckie 2016;Tsourma, Zikos, Drosou, & Tzovaras 2018;Uriarte, Ng, & Moris 2018), employed other optimization techniques, such as (Tsourma et al 2018) that proposed a Task Distribution Engine to automate and optimize the task scheduling and resources assignment procedure in industrial environments.…”

Section: Descriptive Analyticsmentioning

confidence: 99%

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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“…Trends found in many industrial sectors reveal that sensor reliability, condition-based monitoring, anomaly detection, and prediction, prescriptive actions (anticipation), and knowledgebased decision-making will play an important role in smart manufacturing scenarios [21,22]. Furthermore, the combination of unsupervised and supervised leaning, clustering, and metaheuristic techniques, and the new selffunctionalities will yield a new set of methods and know-how for advancing our understanding of these complex cutting-edge industrial manufacturing processes [23,24].…”

Section: Introductionmentioning

confidence: 99%

Cloud-Based Industrial Cyber–Physical System for Data-Driven Reasoning: A Review and Use Case on an Industry 4.0 Pilot Line

Villalonga

Beruvides

Castaño

et al. 2020

IEEE Trans. Ind. Inf.

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Nowadays, reconfiguration and adaptation by means of optimal re-parametrization in industrial cyber-physical systems (ICPS) is one of the bottlenecks for the digital transformation of the manufacturing industry. This work proposes a cloud-to-edgesbased ICPS equipped with machine learning techniques. The proposed reasoning module includes a learning procedure based on two reinforcement learning techniques, running in parallel, for updating both the data-conditioning and processing strategy and the prediction model. The presented solution distributes computational resources and analytic engines in multiple layers and independent modules increasing the smartness and the autonomy for monitoring and control the behavior at shop floor level. The suitability of the proposed solution, evaluated in a pilot line, is endorsed by fast time response (i.e., 0.01s at the edge level) and the appropriate setting of optimal operational parameters for guaranteeing the desired quality surface roughness during macro and micro milling operations.

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Towards Predicting System Disruption in Industry 4.0: Machine Learning-Based Approach

Cited by 41 publications

References 17 publications

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

Business analytics in Industry 4.0: A systematic review

Cloud-Based Industrial Cyber–Physical System for Data-Driven Reasoning: A Review and Use Case on an Industry 4.0 Pilot Line

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