Learning With Imbalanced Data in Smart Manufacturing: A Comparative Analysis
The Internet of Things (IoT) paradigm is revolutionising the world of manufacturing into what is known as Smart Manufacturing or Industry 4.0. The main pillar in smart manufacturing looks at harnessing IoT data and leveraging machine learning (ML) to automate the prediction of faults, thus cutting m...
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doaj-3fcd129445424d2189096501bedbcf142021-03-30T14:58:01ZengIEEEIEEE Access2169-35362021-01-0192734275710.1109/ACCESS.2020.30478389309288Learning With Imbalanced Data in Smart Manufacturing: A Comparative AnalysisYasmin Fathy0https://orcid.org/0000-0001-7398-5283Mona Jaber1https://orcid.org/0000-0002-0908-3207Alexandra Brintrup2https://orcid.org/0000-0002-4189-2434Department of Engineering, University of Cambridge, Cambridge, U.K.School of Electronic Engineering and Computer Science, Queen Mary University of London, London, U.K.Department of Engineering, University of Cambridge, Cambridge, U.K.The Internet of Things (IoT) paradigm is revolutionising the world of manufacturing into what is known as Smart Manufacturing or Industry 4.0. The main pillar in smart manufacturing looks at harnessing IoT data and leveraging machine learning (ML) to automate the prediction of faults, thus cutting maintenance time and cost and improving the product quality. However, faults in real industries are overwhelmingly outweighed by instances of good performance (faultless samples); this bias is reflected in the data captured by IoT devices. Imbalanced data limits the success of ML in predicting faults, thus presents a significant hindrance in the progress of smart manufacturing. Although various techniques have been proposed to tackle this challenge in general, this work is the first to present a framework for evaluating the effectiveness of these remedies in the context of manufacturing. We present a comprehensive comparative analysis in which we apply our proposed framework to benchmark the performance of different combinations of algorithm components using a real-world manufacturing dataset. We draw key insights into the effectiveness of each component and inter-relatedness between the dataset, the application context, and the design of the ML algorithm.https://ieeexplore.ieee.org/document/9309288/Manufacturing analyticsgenerative modelingsmart manufacturingimbalanced datalimited failure datagenerating synthetic data |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yasmin Fathy Mona Jaber Alexandra Brintrup |
spellingShingle |
Yasmin Fathy Mona Jaber Alexandra Brintrup Learning With Imbalanced Data in Smart Manufacturing: A Comparative Analysis IEEE Access Manufacturing analytics generative modeling smart manufacturing imbalanced data limited failure data generating synthetic data |
author_facet |
Yasmin Fathy Mona Jaber Alexandra Brintrup |
author_sort |
Yasmin Fathy |
title |
Learning With Imbalanced Data in Smart Manufacturing: A Comparative Analysis |
title_short |
Learning With Imbalanced Data in Smart Manufacturing: A Comparative Analysis |
title_full |
Learning With Imbalanced Data in Smart Manufacturing: A Comparative Analysis |
title_fullStr |
Learning With Imbalanced Data in Smart Manufacturing: A Comparative Analysis |
title_full_unstemmed |
Learning With Imbalanced Data in Smart Manufacturing: A Comparative Analysis |
title_sort |
learning with imbalanced data in smart manufacturing: a comparative analysis |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2021-01-01 |
description |
The Internet of Things (IoT) paradigm is revolutionising the world of manufacturing into what is known as Smart Manufacturing or Industry 4.0. The main pillar in smart manufacturing looks at harnessing IoT data and leveraging machine learning (ML) to automate the prediction of faults, thus cutting maintenance time and cost and improving the product quality. However, faults in real industries are overwhelmingly outweighed by instances of good performance (faultless samples); this bias is reflected in the data captured by IoT devices. Imbalanced data limits the success of ML in predicting faults, thus presents a significant hindrance in the progress of smart manufacturing. Although various techniques have been proposed to tackle this challenge in general, this work is the first to present a framework for evaluating the effectiveness of these remedies in the context of manufacturing. We present a comprehensive comparative analysis in which we apply our proposed framework to benchmark the performance of different combinations of algorithm components using a real-world manufacturing dataset. We draw key insights into the effectiveness of each component and inter-relatedness between the dataset, the application context, and the design of the ML algorithm. |
topic |
Manufacturing analytics generative modeling smart manufacturing imbalanced data limited failure data generating synthetic data |
url |
https://ieeexplore.ieee.org/document/9309288/ |
work_keys_str_mv |
AT yasminfathy learningwithimbalanceddatainsmartmanufacturingacomparativeanalysis AT monajaber learningwithimbalanceddatainsmartmanufacturingacomparativeanalysis AT alexandrabrintrup learningwithimbalanceddatainsmartmanufacturingacomparativeanalysis |
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1724180202746019840 |