Design, building and deployment of smart applications for anomaly detection and failure prediction in industrial use cases

This paper presents a comparative analysis of deep learning techniques for anomaly detection and failure prediction. We explore various deep learning architectures on an IoT dataset, including recurrent neural networks (RNNs, LSTMs and GRUs), convolutional neural networks (CNNs) and transformers, to...

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Detalhes bibliográficos
Autores: Dintén Herrero, Ricardo, Zorrilla Pantaleón, Marta E.|||0000-0002-0475-8834
Formato: artículo
Fecha de publicación:2024
País:España
Recursos:Universidad de Cantabria (UC)
Repositorio:UCrea Repositorio Abierto de la Universidad de Cantabria
Idioma:inglés
OAI Identifier:oai:repositorio.unican.es:10902/34395
Acesso em linha:https://hdl.handle.net/10902/34395
Access Level:acceso abierto
Palavra-chave:Predictive maintenance
Deep learning
Explainability
Model-based deployment
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spelling Design, building and deployment of smart applications for anomaly detection and failure prediction in industrial use casesDintén Herrero, RicardoZorrilla Pantaleón, Marta E.|||0000-0002-0475-8834Predictive maintenanceDeep learningExplainabilityModel-based deploymentThis paper presents a comparative analysis of deep learning techniques for anomaly detection and failure prediction. We explore various deep learning architectures on an IoT dataset, including recurrent neural networks (RNNs, LSTMs and GRUs), convolutional neural networks (CNNs) and transformers, to assess their effectiveness in anomaly detection and failure prediction. It was found that the hybrid transformer-GRU configuration delivers the highest accuracy, albeit at the cost of requiring the longest computational time for training. Furthermore, we employ explainability techniques to elucidate the decision-making processes of these black box models and evaluate their behaviour. By analysing the inner workings of the models, we aim at providing insights into the factors influencing failure predictions. Through comprehensive experimentation and analysis on sensor data collected from a water pump, this study contributes to the understanding of deep learning methodologies for anomaly detection and failure prediction and underscores the importance of model interpretability in critical applications such as prognostics and health management. Additionally, we specify the architecture for deploying these models in a real environment using the RAI4.0 metamodel, meant for designing, configuring and automatically deploying distributed stream-based industrial applications. Our findings will offer valuable guidance for practitioners seeking to deploy deep learning techniques effectively in predictive maintenance systems, facilitating informed decision-making and enhancing reliability and efficiency in industrial operations.Funded by the Spanish Government and FEDER funds (AEI/FEDER, UE) under grant PID2021-124502OB-C42 (PRESECREL) and the predoctoral program "Concepción Arenal del Programa de Personal Investigador en formación Predoctoral" funded by Universidad de Cantabria and Cantabria's Government (BOC 18-10-2021).MDPIUniversidad de Cantabria20242024-09-01journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articlehttps://hdl.handle.net/10902/34395Information 2024, 15(9), 557reponame:UCrea Repositorio Abierto de la Universidad de Cantabriainstname:Universidad de Cantabria (UC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:repositorio.unican.es:10902/343952026-06-02T12:39:31Z
dc.title.none.fl_str_mv Design, building and deployment of smart applications for anomaly detection and failure prediction in industrial use cases
title Design, building and deployment of smart applications for anomaly detection and failure prediction in industrial use cases
spellingShingle Design, building and deployment of smart applications for anomaly detection and failure prediction in industrial use cases
Dintén Herrero, Ricardo
Predictive maintenance
Deep learning
Explainability
Model-based deployment
title_short Design, building and deployment of smart applications for anomaly detection and failure prediction in industrial use cases
title_full Design, building and deployment of smart applications for anomaly detection and failure prediction in industrial use cases
title_fullStr Design, building and deployment of smart applications for anomaly detection and failure prediction in industrial use cases
title_full_unstemmed Design, building and deployment of smart applications for anomaly detection and failure prediction in industrial use cases
title_sort Design, building and deployment of smart applications for anomaly detection and failure prediction in industrial use cases
dc.creator.none.fl_str_mv Dintén Herrero, Ricardo
Zorrilla Pantaleón, Marta E.|||0000-0002-0475-8834
author Dintén Herrero, Ricardo
author_facet Dintén Herrero, Ricardo
Zorrilla Pantaleón, Marta E.|||0000-0002-0475-8834
author_role author
author2 Zorrilla Pantaleón, Marta E.|||0000-0002-0475-8834
author2_role author
dc.contributor.none.fl_str_mv Universidad de Cantabria
dc.subject.none.fl_str_mv Predictive maintenance
Deep learning
Explainability
Model-based deployment
topic Predictive maintenance
Deep learning
Explainability
Model-based deployment
description This paper presents a comparative analysis of deep learning techniques for anomaly detection and failure prediction. We explore various deep learning architectures on an IoT dataset, including recurrent neural networks (RNNs, LSTMs and GRUs), convolutional neural networks (CNNs) and transformers, to assess their effectiveness in anomaly detection and failure prediction. It was found that the hybrid transformer-GRU configuration delivers the highest accuracy, albeit at the cost of requiring the longest computational time for training. Furthermore, we employ explainability techniques to elucidate the decision-making processes of these black box models and evaluate their behaviour. By analysing the inner workings of the models, we aim at providing insights into the factors influencing failure predictions. Through comprehensive experimentation and analysis on sensor data collected from a water pump, this study contributes to the understanding of deep learning methodologies for anomaly detection and failure prediction and underscores the importance of model interpretability in critical applications such as prognostics and health management. Additionally, we specify the architecture for deploying these models in a real environment using the RAI4.0 metamodel, meant for designing, configuring and automatically deploying distributed stream-based industrial applications. Our findings will offer valuable guidance for practitioners seeking to deploy deep learning techniques effectively in predictive maintenance systems, facilitating informed decision-making and enhancing reliability and efficiency in industrial operations.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-09-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/10902/34395
url https://hdl.handle.net/10902/34395
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv Information 2024, 15(9), 557
reponame:UCrea Repositorio Abierto de la Universidad de Cantabria
instname:Universidad de Cantabria (UC)
instname_str Universidad de Cantabria (UC)
reponame_str UCrea Repositorio Abierto de la Universidad de Cantabria
collection UCrea Repositorio Abierto de la Universidad de Cantabria
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.811543

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