IoT edge computing-enabled collaborative tracking system for manufacturing resources in industrial park

Zhao, Zhiheng; Lin, Peng; Shen, Leidi; Zhang, Mengdi; Huang, George Q.

doi:10.1016/j.aei.2020.101044

Cited by 74 publications

(20 citation statements)

References 45 publications

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“…The techniques described so far considered a fixed edge device. However, the authors in [27] proposed Supervised Learning of Genetic Tracking (SLGT) in an edge architecture(fixed+mobile) for an industrial park where resources are moved from the production phase to the next using trolleys.…”

Section: B Techniques Using Edge-cloud Architecturementioning

confidence: 99%

Edge Intelligence for Data Handling and Predictive Maintenance in IIOT

Hafeez

McArdle

2021

IEEE Access

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The use of IoT has become pervasive and IoT devices are common in many domains. Industrial IoT (IIoT) utilises IoT devices and sensors to monitor machines and environments to ensure optimal performance of equipment and processes. Predictive Maintenance (PM) which monitors the health of machines to determine the probable failure of components is one IIoT technique which is receiving attention lately. To achieve effective PM, massive amounts of data are collected, processed and ultimately analysed by Machine Learning (ML) algorithms. Traditionally IoT sensors transmit their data readings to the cloud for processing and modelling. Handling and transmitting massive amounts of data between IoT devices and infrastructure has a cost. Edge Computing (EC) in which both sensors and intermediate nodes can process data provides opportunities to reduce data transmission costs and increase processing speed. This article examines IIoT for PM and discusses how and where data can be processed and analysed. Initially, this article presents sampling and data reduction techniques. These techniques allow for a reduction in the amount of data transmitted to the cloud for processing but there are potential accuracy trade-offs when ML algorithms utilise reduced datasets. An alternative approach is to move ML algorithms closer to the data to reduce data transmission. There are three main techniques that utilise the EC paradigm to perform ML and data processing on intermediary nodes. These techniques are categorized according to where data processing occurs: Device and Edge, Edge and Cloud and Device and Cloud (Federated Learning). In addition to exploring traditional approaches, these three state-of-the-art techniques are examined in this article and their benefits and weaknesses are presented. A novel architecture to demonstrate how EC can be utilized both for data reduction and PM in IIoT is also proposed.

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Section: B Techniques Using Edge-cloud Architecturementioning

confidence: 99%

Edge Intelligence for Data Handling and Predictive Maintenance in IIOT

Hafeez

McArdle

2021

IEEE Access

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“…Lin et al proposed a smart manufacturing factory framework based on edge computing, and further investigated the job shop scheduling problems under such a framework [20]. In order to provide the right manufacturing resources for subsequent production steps, an IoT edge computing enabled collaborative tracking system was developed to for manufacturing resources in industrial park [21]. For the purpose of recognizing industrial equipment accurately in manufacturing systems, Lai et al adopted the LSTM to analyze big data features and built a nonintrusive load monitoring system, and edge computing was used to implement parallel computing to improve the efficiency of equipment identification [22].…”

Section: A Manufacturing Energy Data Processing and Edge Computingmentioning

confidence: 99%

Edge Computing Enabled Production Anomalies Detection and Energy-Efficient Production Decision Approach for Discrete Manufacturing Workshops

Zhang

2020

IEEE Access

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Due to the complexity and dynamics of manufacturing processes, there are various production anomalies in a discrete manufacturing workshop, which have a strong impact on manufacturing quality and productivity. Meanwhile, with the rapid development of Internet of Things technology and communication technology, data store and timely response become new challenges for production anomalies detection. Thus, an edge computing enabled production anomalies detection and energy-efficient production decision approach is proposed in this study. Firstly, an architecture of edge computing enabled production anomalies detection and energy-efficient rescheduling approach is introduced. Then, considering that the raw energy data are always large, isolated and messy, an energy consumption data preprocessing algorithm is established, and production anomaly analysis model is constructed based on long short-term memory network. When an anomaly occurs, an energy-efficient production decision making will be triggered. Finally, through a case analysis of a milling manufacturing system, the results show that the anomaly detection error of the proposed method is only 3.5%. This method realizes the combination of energy consumption data and manufacturing system anomalies detection, and can further assist production process monitoring and energy conservation. INDEX TERMS Edge computing; production anomalies detection; energy-efficient; long short-term memory network; discrete manufacturing workshops.

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“…Response time, exact fault identification, CPU usage and lower network delay Data tracking is limited [34] The experiment reveals that refining raw IoT data at the edge devices is efficient with respect to latency and imparts circumstantial awareness for the smart city decision-framers in a logical manner.…”

Section: Automobile Manufacturingmentioning

confidence: 99%