Towards Machine Learning and Low Data Rate IoT for Fault Detection in Data Driven Predictive Maintenance

Wesley Bevan Richardson; Johan Meyer; Sune Von Solms

doi:10.1109/AIIoT52608.2021.9454190

Towards Machine Learning and Low Data Rate IoT for Fault Detection in Data Driven Predictive Maintenance

Wesley Bevan Richardson
, Johan Meyer
, Sune Von Solms

Electrical and Electronic Engineering Science

University of Johannesburg

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

10 Citations (Scopus)

Abstract

While predictive maintenance is a concept that has been around for several decades, it is only due to the relatively recent arrival and expeditious development of fourth industrial revolution technologies, such as the internet of things and machine learning, that it has become more of a reality. Rural communities face several challenges in their day to day lives and while several development projects have been enacted to address these problems, many have failed due to a multitude of factors. One of the contributing factors to these rural development projects failing is the lack of or insufficient maintenance. The aim of this study was to show how fault detection in data driven predictive maintenance in remote and rural locations could be achieved using the one-class support vector machines algorithm and low data rate (bandwidth) internet of things. The results of this study show how fault detection in predictive maintenance can be achieved using the one-class support vector machines algorithm and low bandwidth internet of things sensors, for rural applications. The outcome of this study provides a steppingstone to implementing data driven predictive maintenance in remote and rural locations.

Original language	English
Title of host publication	2021 IEEE World AI IoT Congress, AIIoT 2021
Editors	Rajashree Paul
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	202-208
Number of pages	7
ISBN (Electronic)	9781665435680
DOIs	https://doi.org/10.1109/AIIoT52608.2021.9454190
Publication status	Published - 10 May 2021
Event	2021 IEEE World AI IoT Congress, AIIoT 2021 - Virtual, Seattle, United States Duration: 10 May 2021 → 13 May 2021

Publication series

Name	2021 IEEE World AI IoT Congress, AIIoT 2021

Conference

Conference	2021 IEEE World AI IoT Congress, AIIoT 2021
Country/Territory	United States
City	Virtual, Seattle
Period	10/05/21 → 13/05/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

fault detection
internet of things
machine learning
one-class support vector machines
predictive maintenance

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Health Informatics

Access to Document

10.1109/AIIoT52608.2021.9454190

Cite this

Richardson, W. B., Meyer, J., & Von Solms, S. (2021). Towards Machine Learning and Low Data Rate IoT for Fault Detection in Data Driven Predictive Maintenance. In R. Paul (Ed.), 2021 IEEE World AI IoT Congress, AIIoT 2021 (pp. 202-208). Article 9454190 (2021 IEEE World AI IoT Congress, AIIoT 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/AIIoT52608.2021.9454190

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title = "Towards Machine Learning and Low Data Rate IoT for Fault Detection in Data Driven Predictive Maintenance",

abstract = "While predictive maintenance is a concept that has been around for several decades, it is only due to the relatively recent arrival and expeditious development of fourth industrial revolution technologies, such as the internet of things and machine learning, that it has become more of a reality. Rural communities face several challenges in their day to day lives and while several development projects have been enacted to address these problems, many have failed due to a multitude of factors. One of the contributing factors to these rural development projects failing is the lack of or insufficient maintenance. The aim of this study was to show how fault detection in data driven predictive maintenance in remote and rural locations could be achieved using the one-class support vector machines algorithm and low data rate (bandwidth) internet of things. The results of this study show how fault detection in predictive maintenance can be achieved using the one-class support vector machines algorithm and low bandwidth internet of things sensors, for rural applications. The outcome of this study provides a steppingstone to implementing data driven predictive maintenance in remote and rural locations.",

keywords = "fault detection, internet of things, machine learning, one-class support vector machines, predictive maintenance",

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Richardson, WB, Meyer, J & Von Solms, S 2021, Towards Machine Learning and Low Data Rate IoT for Fault Detection in Data Driven Predictive Maintenance. in R Paul (ed.), 2021 IEEE World AI IoT Congress, AIIoT 2021., 9454190, 2021 IEEE World AI IoT Congress, AIIoT 2021, Institute of Electrical and Electronics Engineers Inc., pp. 202-208, 2021 IEEE World AI IoT Congress, AIIoT 2021, Virtual, Seattle, United States, 10/05/21. https://doi.org/10.1109/AIIoT52608.2021.9454190

Towards Machine Learning and Low Data Rate IoT for Fault Detection in Data Driven Predictive Maintenance. / Richardson, Wesley Bevan; Meyer, Johan ; Von Solms, Sune.
2021 IEEE World AI IoT Congress, AIIoT 2021. ed. / Rajashree Paul. Institute of Electrical and Electronics Engineers Inc., 2021. p. 202-208 9454190 (2021 IEEE World AI IoT Congress, AIIoT 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - While predictive maintenance is a concept that has been around for several decades, it is only due to the relatively recent arrival and expeditious development of fourth industrial revolution technologies, such as the internet of things and machine learning, that it has become more of a reality. Rural communities face several challenges in their day to day lives and while several development projects have been enacted to address these problems, many have failed due to a multitude of factors. One of the contributing factors to these rural development projects failing is the lack of or insufficient maintenance. The aim of this study was to show how fault detection in data driven predictive maintenance in remote and rural locations could be achieved using the one-class support vector machines algorithm and low data rate (bandwidth) internet of things. The results of this study show how fault detection in predictive maintenance can be achieved using the one-class support vector machines algorithm and low bandwidth internet of things sensors, for rural applications. The outcome of this study provides a steppingstone to implementing data driven predictive maintenance in remote and rural locations.

AB - While predictive maintenance is a concept that has been around for several decades, it is only due to the relatively recent arrival and expeditious development of fourth industrial revolution technologies, such as the internet of things and machine learning, that it has become more of a reality. Rural communities face several challenges in their day to day lives and while several development projects have been enacted to address these problems, many have failed due to a multitude of factors. One of the contributing factors to these rural development projects failing is the lack of or insufficient maintenance. The aim of this study was to show how fault detection in data driven predictive maintenance in remote and rural locations could be achieved using the one-class support vector machines algorithm and low data rate (bandwidth) internet of things. The results of this study show how fault detection in predictive maintenance can be achieved using the one-class support vector machines algorithm and low bandwidth internet of things sensors, for rural applications. The outcome of this study provides a steppingstone to implementing data driven predictive maintenance in remote and rural locations.

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ER -

Richardson WB, Meyer J , Von Solms S. Towards Machine Learning and Low Data Rate IoT for Fault Detection in Data Driven Predictive Maintenance. In Paul R, editor, 2021 IEEE World AI IoT Congress, AIIoT 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 202-208. 9454190. (2021 IEEE World AI IoT Congress, AIIoT 2021). doi: 10.1109/AIIoT52608.2021.9454190

Towards Machine Learning and Low Data Rate IoT for Fault Detection in Data Driven Predictive Maintenance

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this