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A Novel Data Reduction Approach for Structural Health Monitoring Systems
The massive amount of data generated by structural health monitoring (SHM) systems usually affects the system’s capacity for data transmission and analysis. This paper proposes a novel concept based on the probability theory for data reduction in SHM systems. The beauty salient feature of the propos...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6891479/ https://www.ncbi.nlm.nih.gov/pubmed/31698686 http://dx.doi.org/10.3390/s19224823 |
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author | Bolandi, Hamed Lajnef, Nizar Jiao, Pengcheng Barri, Kaveh Hasni, Hassene Alavi, Amir H. |
author_facet | Bolandi, Hamed Lajnef, Nizar Jiao, Pengcheng Barri, Kaveh Hasni, Hassene Alavi, Amir H. |
author_sort | Bolandi, Hamed |
collection | PubMed |
description | The massive amount of data generated by structural health monitoring (SHM) systems usually affects the system’s capacity for data transmission and analysis. This paper proposes a novel concept based on the probability theory for data reduction in SHM systems. The beauty salient feature of the proposed method is that it alleviates the burden of collecting and analysis of the entire strain data via a relative damage approach. In this methodology, the rate of variation of strain distributions is related to the rate of damage. In order to verify the accuracy of the approach, experimental and numerical studies were conducted on a thin steel plate subjected to cyclic in-plane tension loading. Circular holes with various sizes were made on the plate to define damage states. Rather than measuring the entire strain response, the cumulative durations of strain events at different predefined strain levels were obtained for each damage scenario. Then, the distribution of the calculated cumulative times was used to detect the damage progression. The results show that the presented technique can efficiently detect the damage progression. The damage detection accuracy can be improved by increasing the predefined strain levels. The proposed concept can lead to over 2500% reduction in data storage requirement, which can be particularly important for data generation and data handling in on-line SHM systems. |
format | Online Article Text |
id | pubmed-6891479 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-68914792019-12-18 A Novel Data Reduction Approach for Structural Health Monitoring Systems Bolandi, Hamed Lajnef, Nizar Jiao, Pengcheng Barri, Kaveh Hasni, Hassene Alavi, Amir H. Sensors (Basel) Article The massive amount of data generated by structural health monitoring (SHM) systems usually affects the system’s capacity for data transmission and analysis. This paper proposes a novel concept based on the probability theory for data reduction in SHM systems. The beauty salient feature of the proposed method is that it alleviates the burden of collecting and analysis of the entire strain data via a relative damage approach. In this methodology, the rate of variation of strain distributions is related to the rate of damage. In order to verify the accuracy of the approach, experimental and numerical studies were conducted on a thin steel plate subjected to cyclic in-plane tension loading. Circular holes with various sizes were made on the plate to define damage states. Rather than measuring the entire strain response, the cumulative durations of strain events at different predefined strain levels were obtained for each damage scenario. Then, the distribution of the calculated cumulative times was used to detect the damage progression. The results show that the presented technique can efficiently detect the damage progression. The damage detection accuracy can be improved by increasing the predefined strain levels. The proposed concept can lead to over 2500% reduction in data storage requirement, which can be particularly important for data generation and data handling in on-line SHM systems. MDPI 2019-11-06 /pmc/articles/PMC6891479/ /pubmed/31698686 http://dx.doi.org/10.3390/s19224823 Text en © 2019 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 Bolandi, Hamed Lajnef, Nizar Jiao, Pengcheng Barri, Kaveh Hasni, Hassene Alavi, Amir H. A Novel Data Reduction Approach for Structural Health Monitoring Systems |
title | A Novel Data Reduction Approach for Structural Health Monitoring Systems |
title_full | A Novel Data Reduction Approach for Structural Health Monitoring Systems |
title_fullStr | A Novel Data Reduction Approach for Structural Health Monitoring Systems |
title_full_unstemmed | A Novel Data Reduction Approach for Structural Health Monitoring Systems |
title_short | A Novel Data Reduction Approach for Structural Health Monitoring Systems |
title_sort | novel data reduction approach for structural health monitoring systems |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6891479/ https://www.ncbi.nlm.nih.gov/pubmed/31698686 http://dx.doi.org/10.3390/s19224823 |
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