Design and Implementation of Wireless Sensor Based-Monitoring System for Smart Factory

Lee, Seok Cheol; Jeon, Tae Gun; Hwang, Hyun-Suk; Kim, Chang-Soo

doi:10.1007/978-3-540-74477-1_54

Cited by 14 publications

(13 citation statements)

References 10 publications

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“…In order to implement CPS-based environment, a network system capable of systematically managing collected data using smart objects, such as sensors and actuators and industrial networks, is needed. However, in this study, simulation software is used to collect data because there is no environment that can obtain data from factories through sensors [35,36]. Figure 10 shows implementation of a conveyor belt at the shop-floor.…”

Section: Simulation and Resultsmentioning

confidence: 99%

SVM‐Based Dynamic Reconfiguration CPS for Manufacturing System in Industry 4.0

Shin

Cho

2018

Wireless Communications and Mobile Computing

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CPS is potential application in various fields, such as medical, healthcare, energy, transportation, and defense, as well as Industry 4.0 in Germany. Although studies on the equipment aging and prediction of problem have been done by combining CPS with Industry 4.0, such studies were based on small numbers and majority of the papers focused primarily on CPS methodology. Therefore, it is necessary to study active self-protection to enable self-management functions, such as self-healing by applying CPS in shop-floor. In this paper, we have proposed modeling of shop-floor and a dynamic reconfigurable CPS scheme that can predict the occurrence of anomalies and self-protection in the model. For this purpose, SVM was used as a machine learning technology and it was possible to restrain overloading in manufacturing process. In addition, we design CPS framework based on machine learning for Industry 4.0, simulate it, and perform. Simulation results show the simulation model autonomously detects the abnormal situation and it is dynamically reconfigured through self-healing.

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Section: Simulation and Resultsmentioning

confidence: 99%

SVM‐Based Dynamic Reconfiguration CPS for Manufacturing System in Industry 4.0

Shin

Cho

2018

Wireless Communications and Mobile Computing

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“…A typical manufacturing factory is composed of production facilities and utility equipment, where the main activities are manufacturing operations and equipment maintenance [2]. Traditionally, wired sensors and actuators have been used for factory automation in combination with legacy sensing and control units.…”

Section: A Factory Automationmentioning

confidence: 99%

“…Instead of focusing more on general methodology research, application-specific WSN approaches are investigated in [1] and [2]. The former was motivated by real business cases from the oil and gas industry and focused on interoperability between different firmware systems.…”

Section: Related Workmentioning

confidence: 99%

“…Work in this area has attracted wide interests over the past two decades driven by the potential of WSNs in industrial applications. Because of unique characteristics and technical challenges from both the application and the basic network sides, research and development into DSC middleware has focused on general methodology [9]- [18], as well as application-specific issues [1], [2].…”

Section: Related Workmentioning

confidence: 99%

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Application-Oriented Intelligent Middleware for Distributed Sensing and Control

Cai

Gholami

Yang

et al. 2012

IEEE Trans. Syst., Man, Cybern. C

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In this paper, wireless sensor networks are proposed as a distributed sensing and control (DSC) approach for productivity and safety improvement of harsh and dynamic industrial systems, such as factory automation, oil and gas industries, and wind farms. The proposed approach focuses on DSC middleware, which considers both application requirements and network resource constraints. By embedding complex application knowledge at different levels and configuring network topology in real time, the DSC system can accomplish effective task assignment, optimal network deployment, and device-level intelligence. IEC 61499 function blocks and intelligent agents are employed as modeling tools for the middleware implementation.Index Terms-Distributed sensing and control (DSC), IEC 61499 function blocks, intelligent agents, middleware, wireless sensor networks (WSNs).

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“…Wireless sensor networks (WSNs) have the promising potential to realize both environment monitoring [1] and devices control in industrial plants, which use cheap, simple, compact sensor nodes. At the same time, wearable equipment and mobile phones have opened the door of a novel paradigm for monitoring the large-scale areas (e.g., smart phones have rich sensors such as GPS, microphone, Bluetooth and accelerometer sensor [2]).…”

Section: Introductionmentioning

confidence: 99%

Data Collection Middleware for Crowdsourcing-based Industrial Sensing Intelligence

Huo

Shu

Zhou³

et al. 2015

Proceedings of the ACM International Workshop on Mobility and MiddleWare Management in HetNets

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In this paper, crowdsourcing-based industrial sensing intelligence (CISI) is proposed as a collaborative approach for large-scale monitoring in modern industrial plants, targeting at improved productivity and increased workplace safety. The proposed approach focuses on middleware, which considers both application and industry-grade requirements. Through embedding crowdsourcing knowledge at different levels and supporting QoS services, systems based on CISI can perform effective work assignment and flexible configuration of wireless sensor networks (WSNs). This paper presents a middleware that addresses these characteristics, which is an extension of GSN, our earlier work on middleware for rapid deployment and integration of heterogeneous sensor networks. Wireless sensor devices and wearable equipment are employed as modeling tools for the middleware implementation.

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Design and Implementation of Wireless Sensor Based-Monitoring System for Smart Factory

Cited by 14 publications

References 10 publications

SVM‐Based Dynamic Reconfiguration CPS for Manufacturing System in Industry 4.0

SVM‐Based Dynamic Reconfiguration CPS for Manufacturing System in Industry 4.0

Application-Oriented Intelligent Middleware for Distributed Sensing and Control

Data Collection Middleware for Crowdsourcing-based Industrial Sensing Intelligence

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