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Detecting Faults at the Edge via Sensor Data Fusion Echo State Networks
The pervasive use of sensors and actuators in the Industry 4.0 paradigm has changed the way we interact with industrial systems. In such a context, modern frameworks are not only limited to the system telemetry but also include the detection of potentially harmful conditions. However, when the numbe...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9030568/ https://www.ncbi.nlm.nih.gov/pubmed/35458841 http://dx.doi.org/10.3390/s22082858 |
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author | Bruneo, Dario De Vita, Fabrizio |
author_facet | Bruneo, Dario De Vita, Fabrizio |
author_sort | Bruneo, Dario |
collection | PubMed |
description | The pervasive use of sensors and actuators in the Industry 4.0 paradigm has changed the way we interact with industrial systems. In such a context, modern frameworks are not only limited to the system telemetry but also include the detection of potentially harmful conditions. However, when the number of signals generated by a system is large, it becomes challenging to properly correlate the information for an effective diagnosis. The combination of Artificial Intelligence and sensor data fusion techniques is a valid solution to address this problem, implementing models capable of extracting information from a set of heterogeneous sources. On the other hand, the constrained resources of Edge devices, where these algorithms are usually executed, pose strict limitations in terms of memory occupation and models complexity. To overcome this problem, in this paper we propose an Echo State Network architecture which exploits sensor data fusion to detect the faults on a scale replica industrial plant. Thanks to its sparse weights structure, Echo State Networks are Recurrent Neural Networks models, which exhibit a low complexity and memory footprint, which makes them suitable to be deployed on an Edge device. Through the analysis of vibration and current signals, the proposed model is able to correctly detect the majority of the faults occurring in the industrial plant. Experimental results demonstrate the feasibility of the proposed approach and present a comparison with other approaches, where we show that our methodology is the best trade-off in terms of precision, recall, F1-score and inference time. |
format | Online Article Text |
id | pubmed-9030568 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-90305682022-04-23 Detecting Faults at the Edge via Sensor Data Fusion Echo State Networks Bruneo, Dario De Vita, Fabrizio Sensors (Basel) Article The pervasive use of sensors and actuators in the Industry 4.0 paradigm has changed the way we interact with industrial systems. In such a context, modern frameworks are not only limited to the system telemetry but also include the detection of potentially harmful conditions. However, when the number of signals generated by a system is large, it becomes challenging to properly correlate the information for an effective diagnosis. The combination of Artificial Intelligence and sensor data fusion techniques is a valid solution to address this problem, implementing models capable of extracting information from a set of heterogeneous sources. On the other hand, the constrained resources of Edge devices, where these algorithms are usually executed, pose strict limitations in terms of memory occupation and models complexity. To overcome this problem, in this paper we propose an Echo State Network architecture which exploits sensor data fusion to detect the faults on a scale replica industrial plant. Thanks to its sparse weights structure, Echo State Networks are Recurrent Neural Networks models, which exhibit a low complexity and memory footprint, which makes them suitable to be deployed on an Edge device. Through the analysis of vibration and current signals, the proposed model is able to correctly detect the majority of the faults occurring in the industrial plant. Experimental results demonstrate the feasibility of the proposed approach and present a comparison with other approaches, where we show that our methodology is the best trade-off in terms of precision, recall, F1-score and inference time. MDPI 2022-04-08 /pmc/articles/PMC9030568/ /pubmed/35458841 http://dx.doi.org/10.3390/s22082858 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 Bruneo, Dario De Vita, Fabrizio Detecting Faults at the Edge via Sensor Data Fusion Echo State Networks |
title | Detecting Faults at the Edge via Sensor Data Fusion Echo State Networks |
title_full | Detecting Faults at the Edge via Sensor Data Fusion Echo State Networks |
title_fullStr | Detecting Faults at the Edge via Sensor Data Fusion Echo State Networks |
title_full_unstemmed | Detecting Faults at the Edge via Sensor Data Fusion Echo State Networks |
title_short | Detecting Faults at the Edge via Sensor Data Fusion Echo State Networks |
title_sort | detecting faults at the edge via sensor data fusion echo state networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9030568/ https://www.ncbi.nlm.nih.gov/pubmed/35458841 http://dx.doi.org/10.3390/s22082858 |
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