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Sensitivity Analysis of Sensors in a Hydraulic Condition Monitoring System Using CNN Models
Condition monitoring (CM) is a useful application in industry 4.0, where the machine’s health is controlled by computational intelligence methods. Data-driven models, especially from the field of deep learning, are efficient solutions for the analysis of time series sensor data due to their ability...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7308971/ https://www.ncbi.nlm.nih.gov/pubmed/32532058 http://dx.doi.org/10.3390/s20113307 |
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author | König, Caroline Helmi, Ahmed Mohamed |
author_facet | König, Caroline Helmi, Ahmed Mohamed |
author_sort | König, Caroline |
collection | PubMed |
description | Condition monitoring (CM) is a useful application in industry 4.0, where the machine’s health is controlled by computational intelligence methods. Data-driven models, especially from the field of deep learning, are efficient solutions for the analysis of time series sensor data due to their ability to recognize patterns in high dimensional data and to track the temporal evolution of the signal. Despite the excellent performance of deep learning models in many applications, additional requirements regarding the interpretability of machine learning models are getting relevant. In this work, we present a study on the sensitivity of sensors in a deep learning based CM system providing high-level information about the relevance of the sensors. Several convolutional neural networks (CNN) have been constructed from a multisensory dataset for the prediction of different degradation states in a hydraulic system. An attribution analysis of the input features provided insights about the contribution of each sensor in the prediction of the classifier. Relevant sensors were identified, and CNN models built on the selected sensors resulted equal in prediction quality to the original models. The information about the relevance of sensors is useful for the system’s design to decide timely on the required sensors. |
format | Online Article Text |
id | pubmed-7308971 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-73089712020-06-25 Sensitivity Analysis of Sensors in a Hydraulic Condition Monitoring System Using CNN Models König, Caroline Helmi, Ahmed Mohamed Sensors (Basel) Article Condition monitoring (CM) is a useful application in industry 4.0, where the machine’s health is controlled by computational intelligence methods. Data-driven models, especially from the field of deep learning, are efficient solutions for the analysis of time series sensor data due to their ability to recognize patterns in high dimensional data and to track the temporal evolution of the signal. Despite the excellent performance of deep learning models in many applications, additional requirements regarding the interpretability of machine learning models are getting relevant. In this work, we present a study on the sensitivity of sensors in a deep learning based CM system providing high-level information about the relevance of the sensors. Several convolutional neural networks (CNN) have been constructed from a multisensory dataset for the prediction of different degradation states in a hydraulic system. An attribution analysis of the input features provided insights about the contribution of each sensor in the prediction of the classifier. Relevant sensors were identified, and CNN models built on the selected sensors resulted equal in prediction quality to the original models. The information about the relevance of sensors is useful for the system’s design to decide timely on the required sensors. MDPI 2020-06-10 /pmc/articles/PMC7308971/ /pubmed/32532058 http://dx.doi.org/10.3390/s20113307 Text en © 2020 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article König, Caroline Helmi, Ahmed Mohamed Sensitivity Analysis of Sensors in a Hydraulic Condition Monitoring System Using CNN Models |
title | Sensitivity Analysis of Sensors in a Hydraulic Condition Monitoring System Using CNN Models |
title_full | Sensitivity Analysis of Sensors in a Hydraulic Condition Monitoring System Using CNN Models |
title_fullStr | Sensitivity Analysis of Sensors in a Hydraulic Condition Monitoring System Using CNN Models |
title_full_unstemmed | Sensitivity Analysis of Sensors in a Hydraulic Condition Monitoring System Using CNN Models |
title_short | Sensitivity Analysis of Sensors in a Hydraulic Condition Monitoring System Using CNN Models |
title_sort | sensitivity analysis of sensors in a hydraulic condition monitoring system using cnn models |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7308971/ https://www.ncbi.nlm.nih.gov/pubmed/32532058 http://dx.doi.org/10.3390/s20113307 |
work_keys_str_mv | AT konigcaroline sensitivityanalysisofsensorsinahydraulicconditionmonitoringsystemusingcnnmodels AT helmiahmedmohamed sensitivityanalysisofsensorsinahydraulicconditionmonitoringsystemusingcnnmodels |