Edge Computing and Distributed Ledger Technologies for Flexible Production Lines: A White-Appliances Industry Case

Petrali, Pierluigi; Isaja, Mauro; Soldatos, John

doi:10.1016/j.ifacol.2018.08.324

Cited by 9 publications

(5 citation statements)

References 3 publications

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“…Syafrudin et al [ 35 ] set up an edge system on an automotive assembly line to obtain environmental data from sensors such as gyroscopes and accelerometers for processing and rapid fault identification. Petrali et al [ 36 ] used edge computing to analyze the sorter status in real-time and intelligently controlled the conveyor belt by sharing statuses to improve sorting efficiency and accuracy. Zhe et al [ 37 ] developed an intelligent monitoring system for underground coal mines, which shortened the response time and significantly improved safety by reducing the transmission link to the cloud center.…”

Section: Related Workmentioning

confidence: 99%

“…The methods described in the references [ 19 , 22 , 25 , 26 , 27 ] for current-based monitoring are rarely employed to acquire production data. According to the research in references [ 33 , 34 , 35 , 36 , 37 , 38 , 39 ], edge computing has been more frequently employed for equipment monitoring. The references [ 40 , 41 ] show that deep learning augmentation algorithms have better recognition results in the case of insufficient training data.…”

Section: Related Workmentioning

confidence: 99%

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Monitoring the Production Information of Conventional Machining Equipment Based on Edge Computing

Wang

Shen

Zhu

et al. 2022

Sensors

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A production status monitoring method based on edge computing is proposed for traditional machining offline equipment to address the deficiencies that traditional machining offline equipment have, which cannot automatically count the number of parts produced, obtain part processing time information, and discern anomalous operation status. Firstly, the total current signal of the collected equipment was filtered to extract the processing segment data. The processing segment data were then used to manually calibrate the feature vector of the equipment for specific parts and processes, and the feature vector was used as a reference to match with the real-time electric current data on the edge device to identify and obtain the processing start time, processing end time, and anomalous marks for each part. Finally, the information was uploaded to further obtain the part processing time, loading and unloading standby time, and the cause of the anomaly. To verify the reliability of the method, a prototype system was built, and extensive experiments were conducted on many different types of equipment in an auto parts manufacturer. The experimental results show that the proposed monitoring algorithm based on the calibration vector can stably and effectively identify the production information of each part on an independently developed edge device.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Monitoring the Production Information of Conventional Machining Equipment Based on Edge Computing

Wang

Shen

Zhu

et al. 2022

Sensors

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“…The description of this novel blockchain-based configuration and synchronization is beyond the scope of this paper. Instead, interested readers can consult [22,23]. [16]).…”

Section: Dda Overviewmentioning

confidence: 99%

Configurable Distributed Data Management for the Internet of the Things

2019

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One of the main challenges in modern Internet of Things (IoT) systems is the efficient collection, routing and management of data streams from heterogeneous sources, including sources with high ingestion rates. Despite the existence of various IoT data streaming frameworks, there is still no easy way for collecting and routing IoT streams in efficient and configurable ways that are easy to be implemented and deployed in realistic environments. In this paper, we introduce a programmable engine for Distributed Data Analytics (DDA), which eases the task of collecting IoT streams from different sources and accordingly, routing them to appropriate consumers. The engine provides also the means for preprocessing and analysis of data streams, which are two of the most important tasks in Big Data analytics applications. At the heart of the engine lies a Domain Specific Language (DSL) that enables the zero-programming definition of data routing and preprocessing tasks. This DSL is outlined in the paper, along with the middleware that supports its runtime execution. As part of the paper, we present the architecture of the engine, as well as the digital models that it uses for modelling data streams in the digital world. We also discuss the validation of the DDA in several data intensive IoT use cases in industrial environments, including use cases in pilot productions lines and in several real-life manufacturing environments. The latter manifest the configurability, programmability and flexibility of the DDA engine, as well as its ability to support practical applications.

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“…Their results demonstrated positive impacts from adopting the proposed approach for a real factory. Petrali et al developed a flexible production line edge computing system for a white-appliances industry, using edge computing to process the order and communicate with other devices [23]. Their results showed that the developed model improved system productivity and decreased reconfiguration costs.…”

Section: Edge Computing For Warning Systemsmentioning

confidence: 99%

An Affordable Fast Early Warning System for Edge Computing in Assembly Line

et al. 2018

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Maintaining product quality is essential for smart factories, hence detecting abnormal events in assembly line is important for timely decision-making. This study proposes an affordable fast early warning system based on edge computing to detect abnormal events during assembly line. The proposed model obtains environmental data from various sensors including gyroscopes, accelerometers, temperature, humidity, ambient light, and air quality. The fault model is installed close to the facilities, so abnormal events can be timely detected. Several performance evaluations are conducted to obtain the optimal scenario for utilizing edge devices to improve data processing and analysis speed, and the final proposed model provides the highest accuracy in terms of detecting abnormal events compared to other classification models. The proposed model was tested over four months of operation in a Korean automobile parts factory, and provided significant benefits from monitoring assembly line, as well as classifying abnormal events. The model helped improve decision-making by reducing or preventing unexpected losses due to abnormal events.

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Edge Computing and Distributed Ledger Technologies for Flexible Production Lines: A White-Appliances Industry Case

Cited by 9 publications

References 3 publications

Monitoring the Production Information of Conventional Machining Equipment Based on Edge Computing

Monitoring the Production Information of Conventional Machining Equipment Based on Edge Computing

Configurable Distributed Data Management for the Internet of the Things

An Affordable Fast Early Warning System for Edge Computing in Assembly Line

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