A flexible data schema and system architecture for the virtualization of manufacturing machines (VMM)

Angrish, Atin; Starly, Binil; Lee, Yuan‐Shin; Cohen, Paul H.

doi:10.1016/j.jmsy.2017.10.003

Cited by 75 publications

(27 citation statements)

References 23 publications

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“…The experimental results showed that the proposed system was scalable and efficient enough in processing profitable-area queries from huge amounts of big taxi trip data. Finally, Angrish et al proposed a flexible data schema based on NoSQL MongoDB for the virtualization of manufacturing machines [ 37 ]. The proposed system was evaluated against several query statements.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Big data analysis has led to significant improvements in the manufacturing industry, such as reducing energy consumption [ 17 ], improving production scheduling and logistics planning [ 18 ], mitigating social risks [ 19 ], and facilitating better decision making [ 20 ]. Previous studies have shown significant benefits from several big data technologies in processing and storing large volumes of data quickly, such as with the application of Apache Kafka [ 21 , 22 , 23 , 24 , 25 , 26 ], Apache Storm [ 27 , 28 , 29 , 30 , 31 ], and NoSQL MongoDB [ 32 , 33 , 34 , 35 , 36 , 37 ]. Previous studies showed significant advantages from the integration of big data technologies such as reducing the processing time for home automation systems [ 38 ], providing effective and efficient solutions for processing IoT-generated data for smart cities [ 39 ], and handling large amounts of smart environmental data in real-time [ 40 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing

Syafrudin

Alfian

Fitriyani

et al. 2018

Sensors

262

103

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With the increase in the amount of data captured during the manufacturing process, monitoring systems are becoming important factors in decision making for management. Current technologies such as Internet of Things (IoT)-based sensors can be considered a solution to provide efficient monitoring of the manufacturing process. In this study, a real-time monitoring system that utilizes IoT-based sensors, big data processing, and a hybrid prediction model is proposed. Firstly, an IoT-based sensor that collects temperature, humidity, accelerometer, and gyroscope data was developed. The characteristics of IoT-generated sensor data from the manufacturing process are: real-time, large amounts, and unstructured type. The proposed big data processing platform utilizes Apache Kafka as a message queue, Apache Storm as a real-time processing engine and MongoDB to store the sensor data from the manufacturing process. Secondly, for the proposed hybrid prediction model, Density-Based Spatial Clustering of Applications with Noise (DBSCAN)-based outlier detection and Random Forest classification were used to remove outlier sensor data and provide fault detection during the manufacturing process, respectively. The proposed model was evaluated and tested at an automotive manufacturing assembly line in Korea. The results showed that IoT-based sensors and the proposed big data processing system are sufficiently efficient to monitor the manufacturing process. Furthermore, the proposed hybrid prediction model has better fault prediction accuracy than other models given the sensor data as input. The proposed system is expected to support management by improving decision-making and will help prevent unexpected losses caused by faults during the manufacturing process.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing

Syafrudin

Alfian

Fitriyani

et al. 2018

Sensors

262

103

View full text Add to dashboard Cite

show abstract

“…Many efforts have been made for the IoT data acquisition, processing, and storage, especially in the last two decades [17]. The major objective is to efficiently acquire data produced by heterogeneous and distributed devices, aggregate this data, and store it for later use.…”

Section: Background and Related Workmentioning

confidence: 99%

Framework of an IoT-based Industrial Data Management for Smart Manufacturing

Saqlain

Piao

Shim

et al. 2019

JSAN

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The Internet of Things (IoT) is the global network of interrelated physical devices such as sensors, actuators, smart applications, objects, computing devices, mechanical machines, and people that are becoming an essential part of the internet. In an industrial environment, these devices are the source of data which provide abundant information in manufacturing processes. Nevertheless, the massive, heterogeneous, and time-sensitive nature of the data brings substantial challenges to the real-time collection, processing, and decision making. Therefore, this paper presents a framework of an IoT-based Industrial Data Management System (IDMS) which can manage the huge industrial data, support online monitoring, and control smart manufacturing. The framework contains five basic layers such as physical, network, middleware, database, and application layers to provide a service-oriented architecture for the end users. Experimental results from a smart factory case study demonstrate that the framework can manage the regular data and urgent events generated from various factory devices in the distributed industrial environment through state-of-the-art communication protocols. The collected data is converted into useful information which improves productivity and the prognosis of production lines.

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“…Since the IoT in the manufacturing field contains plenty of physical machines furnished with heterogeneous systems in the form of configurations, models, and semantics, it is inevitable to shield such heterogeneous systems to provide uniform standard service. Here, a virtual machine technique [32,33] is employed to resolve such problems. It maps the physical machines distributed in scattered geographical locations to virtual machines on the cloud platform.…”

Section: Overview Of the Vmementioning

confidence: 99%

A Novel Multi-Agent-Based Collaborative Virtual Manufacturing Environment Integrated with Edge Computing Technique

et al. 2019

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This paper proposes a multi-agent-based collaborative virtual manufacturing environment (VME) to save energy consumption and improve efficiency in the manufacturing process. In order to achieve the high autonomy of the manufacturing system, a multi-agent system (MAS) is designed to build a collaborative VME. In this new VME environment, edge computing is embedded to strengthen the cyber resource utilization and system economy. Moreover, an efficient communication channel between networks is proposed. The subsequent cooperation and collaboration protocols among agents are designed to ensure flexible and process-oriented operations. Furthermore, the fuzzy resolution algorithm is employed to resolve the competition conflicts among function-similar MASs in the distributed manufacturing scenario. Lastly, a simulation and case study are performed to evaluate the performance of the proposed VME in Internet of Things (IoT)-based manufacturing. The analysis results have demonstrated the feasibility and effectiveness of the proposed VME system.

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A flexible data schema and system architecture for the virtualization of manufacturing machines (VMM)

Cited by 75 publications

References 23 publications

Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing

Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing

Framework of an IoT-based Industrial Data Management for Smart Manufacturing

A Novel Multi-Agent-Based Collaborative Virtual Manufacturing Environment Integrated with Edge Computing Technique

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