IIoT Edge Analytics: Deploying Machine Learning at the Wellhead to Identify Rod Pump Failure

Boguslawski, Bartosz; Boujonnier, Matthieu; Bissuel-Beauvais, Loryne; Saghir, Fahd; Sharma, Rajesh

doi:10.2118/192513-ms

Cited by 15 publications

(9 citation statements)

References 10 publications

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“…Thirdly, where to deploy the prediction model is not consistent in every application. For instance, in a hazardous production facility, prediction on the device or the edge is more important than in the cloud [66] to combat any latency issues. Unfortunately, deploying ML in an IoT system faces challenges due to constraints of the IoT system.…”

Section: Edge Computing Based and Machine Learning Enabled Approachesmentioning

confidence: 99%

“…Their proposed hybrid architecture consists of an edge server and sensing device. Similar to [26] and [66], they also utilise image processing as a use case. Image data can also be converted to time-series text data with the help of EC.…”

Section: A Techniques Using Device-edge Architecturementioning

confidence: 99%

“…First, the architecture requires fewer communication resources as well as less burden on cloud resources. Second, if designed to work independently from the cloud, techniques can even work when there is no connection at all [66]. Third, the edge offers more context awareness as compared to cloud-based systems [19].…”

Section: ) Observations On Device-edge Architecturementioning

confidence: 99%

“…Authors in [66] proposed to monitor the real-time data from sensors deployed on equipment in oil/petroleum wells. The goal is to use ML to monitor the equipment performance especially in cases where there is no connectivity available between the site and the back-end cloud, thereby solely depending on the edge.…”

Section: B Techniques Using Edge-cloud Architecturementioning

confidence: 99%

“…Then in the final step, when a mobile/sensor device is aware of the delays and accuracy of all the servers (edge+cloud), it sorts the servers in descending order based on accuracy and selects the first one. Similar to [66], it also works on image-based data.…”

Section: B Techniques Using Edge-cloud Architecturementioning

confidence: 99%

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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: Edge Computing Based and Machine Learning Enabled Approachesmentioning

confidence: 99%

Section: A Techniques Using Device-edge Architecturementioning

confidence: 99%

Section: ) Observations On Device-edge Architecturementioning

confidence: 99%

Section: B Techniques Using Edge-cloud Architecturementioning

confidence: 99%

Section: B Techniques Using Edge-cloud Architecturementioning

confidence: 99%

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Edge Intelligence for Data Handling and Predictive Maintenance in IIOT

Hafeez

McArdle

2021

IEEE Access

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MEC-enabled 5G Use Cases: A Survey on Security Vulnerabilities and Countermeasures

2021

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The future of mobile and internet technologies are manifesting advancements beyond the existing scope of science. The concepts of automated driving, augmented-reality, and machine-type-communication are quite sophisticated and require an elevation of the current mobile infrastructure for launching. The fifth-generation (5G) mobile technology serves as the solution, though it lacks a proximate networking infrastructure to satisfy the service guarantees. Multi-access Edge Computing (MEC) envisages such an edge computing platform. In this survey, we are revealing security vulnerabilities of key 5G-based use cases deployed in the MEC context. Probable security flows of each case are specified, while countermeasures are proposed for mitigating them.

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Benefits of Machine Learning in the Manufacturing Industry

Ailisto¹,

Helaakoski²,

Neuvonen³

2023

Preprint

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Artificial intelligence (AI) technologies, particularly the subfield of machine learning (ML), has been expected to bring significant benefits to all sectors of business and public services. The manufacturing industry is considered one of the domains most likely to benefit from AI tech-nologies. During the recent years, there has been a growing research and development effort on machine learning–based solutions for manufacturing industries, as shown by the growing number of research publications. However, the business, environmental or societal benefits of applying ML in manufacturing industry have not been reviewed and evaluated. This paper re-views the reported benefits of ML in the manufacturing industry by analysis of scientific publi-cations. The focus of the review is on research papers that were published from 2017 to 2021. The review pays particular attention to the industry sector, manufacturing operations, machine learning methods used, maturity of results and the benefits pursued and achieved. The benefits are divided into business benefits, environmental sustainability, and societal sustainability. The analysis shows that the maturity of ML-based solutions is still low, despite the intensive research. Consequently, future research projects should place more emphasis on the maturity of the solu-tions, which would lead to their broader adoption into operational use, thus benefiting industry and society at large. This calls for closer collaboration between the research community and in-dustry.

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IIoT Edge Analytics: Deploying Machine Learning at the Wellhead to Identify Rod Pump Failure

Cited by 15 publications

References 10 publications

Edge Intelligence for Data Handling and Predictive Maintenance in IIOT

Edge Intelligence for Data Handling and Predictive Maintenance in IIOT

MEC-enabled 5G Use Cases: A Survey on Security Vulnerabilities and Countermeasures

Benefits of Machine Learning in the Manufacturing Industry

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