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A New Multi-Sensor Stream Data Augmentation Method for Imbalanced Learning in Complex Manufacturing Process

Multiple sensors are often mounted in a complex manufacturing process to detect failures. Due to the high reliability of modern manufacturing processes, failures only happen occasionally. Therefore, data collected in practical manufacturing processes are extremely imbalanced, which often brings abou...

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Detalles Bibliográficos
Autores principales: Xu, Dongting, Zhang, Zhisheng, Shi, Jinfei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9185280/
https://www.ncbi.nlm.nih.gov/pubmed/35684662
http://dx.doi.org/10.3390/s22114042
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author Xu, Dongting
Zhang, Zhisheng
Shi, Jinfei
author_facet Xu, Dongting
Zhang, Zhisheng
Shi, Jinfei
author_sort Xu, Dongting
collection PubMed
description Multiple sensors are often mounted in a complex manufacturing process to detect failures. Due to the high reliability of modern manufacturing processes, failures only happen occasionally. Therefore, data collected in practical manufacturing processes are extremely imbalanced, which often brings about bias of supervised learning models. Data collected by the multiple sensors can be regarded as multivariate time series or multi-sensor stream data. The high dimension of multi-sensor stream data makes building models even more challenging. In this study, a new and easy-to-apply data augmentation approach, namely, imbalanced multi-sensor stream data augmentation (IMSDA), is proposed for imbalanced learning. IMSDA can generate high quality of failure data for all dimensions. The generated data can keep the similar temporal property of the original multivariate time series. Both raw data and generated data are used to train the failure detection models, but the models are tested by the same real dataset. The proposed method is applied to a real-world industry case. Results show that IMSDA can not only obtain good quality failure data to reduce the imbalance level but also significantly improve the performance of supervised failure detection models.
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spelling pubmed-91852802022-06-11 A New Multi-Sensor Stream Data Augmentation Method for Imbalanced Learning in Complex Manufacturing Process Xu, Dongting Zhang, Zhisheng Shi, Jinfei Sensors (Basel) Article Multiple sensors are often mounted in a complex manufacturing process to detect failures. Due to the high reliability of modern manufacturing processes, failures only happen occasionally. Therefore, data collected in practical manufacturing processes are extremely imbalanced, which often brings about bias of supervised learning models. Data collected by the multiple sensors can be regarded as multivariate time series or multi-sensor stream data. The high dimension of multi-sensor stream data makes building models even more challenging. In this study, a new and easy-to-apply data augmentation approach, namely, imbalanced multi-sensor stream data augmentation (IMSDA), is proposed for imbalanced learning. IMSDA can generate high quality of failure data for all dimensions. The generated data can keep the similar temporal property of the original multivariate time series. Both raw data and generated data are used to train the failure detection models, but the models are tested by the same real dataset. The proposed method is applied to a real-world industry case. Results show that IMSDA can not only obtain good quality failure data to reduce the imbalance level but also significantly improve the performance of supervised failure detection models. MDPI 2022-05-26 /pmc/articles/PMC9185280/ /pubmed/35684662 http://dx.doi.org/10.3390/s22114042 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Xu, Dongting
Zhang, Zhisheng
Shi, Jinfei
A New Multi-Sensor Stream Data Augmentation Method for Imbalanced Learning in Complex Manufacturing Process
title A New Multi-Sensor Stream Data Augmentation Method for Imbalanced Learning in Complex Manufacturing Process
title_full A New Multi-Sensor Stream Data Augmentation Method for Imbalanced Learning in Complex Manufacturing Process
title_fullStr A New Multi-Sensor Stream Data Augmentation Method for Imbalanced Learning in Complex Manufacturing Process
title_full_unstemmed A New Multi-Sensor Stream Data Augmentation Method for Imbalanced Learning in Complex Manufacturing Process
title_short A New Multi-Sensor Stream Data Augmentation Method for Imbalanced Learning in Complex Manufacturing Process
title_sort new multi-sensor stream data augmentation method for imbalanced learning in complex manufacturing process
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9185280/
https://www.ncbi.nlm.nih.gov/pubmed/35684662
http://dx.doi.org/10.3390/s22114042
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