Creation of Anomaly Detection Model for IoT Thermometer Used in Hospital Refrigerators

Detalhes bibliográficos
Autor(a) principal: da Silva, Diego Mendes
Data de Publicação: 2021
Outros Autores: Nunes, Ingrid Bruno, da Silva, Selton Felipe Guedes, Alves, Elyr Teixeira
Tipo de documento: Artigo
Idioma: por
Título da fonte: Revista de Engenharia e Pesquisa Aplicada
Texto Completo: http://revistas.poli.br/index.php/repa/article/view/2159
Resumo: Hospital environments need hospital refrigerators to store drugs, vaccines, blood bags, among others. Such equipment is configured in order to maintain a certain temperature range, since the stored products are sensitive to temperature changes beyond this range. This project aims to analyse the temperature variations beyond adequate. In the experiments performed, different anomaly detection techniques were implemented, using three clustering methods: k-means, DBSCAN and Isolation Forest. Taking into account the accuracy found (76.7%), the method used was DBSCAN. With the analysis performed, it was possible to see several relationships between the temperature values, the number of alerts and the times they happened. It was observed that most of the anomalies found happened between 6:00 and 8:00 am, coinciding with the shift change time between employees.
id UFPE-2_2498782ef96d35cbb0b9b687fe7a9308
oai_identifier_str oai:ojs.poli.br:article/2159
network_acronym_str UFPE-2
network_name_str Revista de Engenharia e Pesquisa Aplicada
repository_id_str
spelling Creation of Anomaly Detection Model for IoT Thermometer Used in Hospital RefrigeratorsCriação de Modelo de Detecção de Anomalias para Termômetro IoT Usado em Refrigeradores HospitalaresHospital environments need hospital refrigerators to store drugs, vaccines, blood bags, among others. Such equipment is configured in order to maintain a certain temperature range, since the stored products are sensitive to temperature changes beyond this range. This project aims to analyse the temperature variations beyond adequate. In the experiments performed, different anomaly detection techniques were implemented, using three clustering methods: k-means, DBSCAN and Isolation Forest. Taking into account the accuracy found (76.7%), the method used was DBSCAN. With the analysis performed, it was possible to see several relationships between the temperature values, the number of alerts and the times they happened. It was observed that most of the anomalies found happened between 6:00 and 8:00 am, coinciding with the shift change time between employees.Ambientes hospitalares precisam de refrigeradores hospitalares para armazenar fármacos, vacinas, bolsas de sangue, dentre outros. Tais equipamentos são configurados de forma a manter determinada faixa de temperatura, visto que os produtos armazenados são sensíveis a mudanças de temperatura fora dessa faixa. Este projeto objetiva analisar as variações de temperatura acima do adequado. Nos experimentos realizados foram implementados diferentes técnicas de detecção de anomalias utilizando três métodos de agrupamento: k-means, DBSCAN e Isolation Forest. Levando em consideração a acurácia encontrada (76,7%), o método utilizado foi o DBSCAN. Com a análise realizada, foi possível perceber diversas relações entre os valores de temperatura, quantidade de alertas e os horários que eles aconteceram. Observou-se que a maior parte das anomalias encontradas aconteceram entre às 6:00 e às 8:00 horas da manhã, coincidindo com o horário de troca de turnos entre funcionários.Escola Politécnica de Pernambuco2021-11-20info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdftext/htmlhttp://revistas.poli.br/index.php/repa/article/view/215910.25286/repa.v6i5.2159Journal of Engineering and Applied Research; Vol 6 No 5 (2021): Edição Especial em Ciência de Dados e Analytics; 120-128Revista de Engenharia e Pesquisa Aplicada; v. 6 n. 5 (2021): Edição Especial em Ciência de Dados e Analytics; 120-1282525-425110.25286/repa.v6i5reponame:Revista de Engenharia e Pesquisa Aplicadainstname:Universidade Federal de Pernambuco (UFPE)instacron:UFPEporhttp://revistas.poli.br/index.php/repa/article/view/2159/796http://revistas.poli.br/index.php/repa/article/view/2159/797-Copyright (c) 2021 Diego Mendes da Silva, Ingrid Bruno Nunes, Selton Felipe Guedes da Silva, Elyr Teixeira Alveshttp://creativecommons.org/licenses/by-nc/4.0info:eu-repo/semantics/openAccessda Silva, Diego MendesNunes, Ingrid Brunoda Silva, Selton Felipe GuedesAlves, Elyr Teixeira2021-11-25T12:22:35Zoai:ojs.poli.br:article/2159Revistahttp://revistas.poli.br/index.php/repaONGhttp://revistas.poli.br/index.php/repa/oai||repa@poli.br2525-42512525-4251opendoar:2021-11-25T12:22:35Revista de Engenharia e Pesquisa Aplicada - Universidade Federal de Pernambuco (UFPE)false
dc.title.none.fl_str_mv Creation of Anomaly Detection Model for IoT Thermometer Used in Hospital Refrigerators
Criação de Modelo de Detecção de Anomalias para Termômetro IoT Usado em Refrigeradores Hospitalares
title Creation of Anomaly Detection Model for IoT Thermometer Used in Hospital Refrigerators
spellingShingle Creation of Anomaly Detection Model for IoT Thermometer Used in Hospital Refrigerators
da Silva, Diego Mendes
title_short Creation of Anomaly Detection Model for IoT Thermometer Used in Hospital Refrigerators
title_full Creation of Anomaly Detection Model for IoT Thermometer Used in Hospital Refrigerators
title_fullStr Creation of Anomaly Detection Model for IoT Thermometer Used in Hospital Refrigerators
title_full_unstemmed Creation of Anomaly Detection Model for IoT Thermometer Used in Hospital Refrigerators
title_sort Creation of Anomaly Detection Model for IoT Thermometer Used in Hospital Refrigerators
author da Silva, Diego Mendes
author_facet da Silva, Diego Mendes
Nunes, Ingrid Bruno
da Silva, Selton Felipe Guedes
Alves, Elyr Teixeira
author_role author
author2 Nunes, Ingrid Bruno
da Silva, Selton Felipe Guedes
Alves, Elyr Teixeira
author2_role author
author
author
dc.contributor.author.fl_str_mv da Silva, Diego Mendes
Nunes, Ingrid Bruno
da Silva, Selton Felipe Guedes
Alves, Elyr Teixeira
description Hospital environments need hospital refrigerators to store drugs, vaccines, blood bags, among others. Such equipment is configured in order to maintain a certain temperature range, since the stored products are sensitive to temperature changes beyond this range. This project aims to analyse the temperature variations beyond adequate. In the experiments performed, different anomaly detection techniques were implemented, using three clustering methods: k-means, DBSCAN and Isolation Forest. Taking into account the accuracy found (76.7%), the method used was DBSCAN. With the analysis performed, it was possible to see several relationships between the temperature values, the number of alerts and the times they happened. It was observed that most of the anomalies found happened between 6:00 and 8:00 am, coinciding with the shift change time between employees.
publishDate 2021
dc.date.none.fl_str_mv 2021-11-20
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://revistas.poli.br/index.php/repa/article/view/2159
10.25286/repa.v6i5.2159
url http://revistas.poli.br/index.php/repa/article/view/2159
identifier_str_mv 10.25286/repa.v6i5.2159
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv http://revistas.poli.br/index.php/repa/article/view/2159/796
http://revistas.poli.br/index.php/repa/article/view/2159/797
dc.rights.driver.fl_str_mv http://creativecommons.org/licenses/by-nc/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
text/html
dc.coverage.none.fl_str_mv -
dc.publisher.none.fl_str_mv Escola Politécnica de Pernambuco
publisher.none.fl_str_mv Escola Politécnica de Pernambuco
dc.source.none.fl_str_mv Journal of Engineering and Applied Research; Vol 6 No 5 (2021): Edição Especial em Ciência de Dados e Analytics; 120-128
Revista de Engenharia e Pesquisa Aplicada; v. 6 n. 5 (2021): Edição Especial em Ciência de Dados e Analytics; 120-128
2525-4251
10.25286/repa.v6i5
reponame:Revista de Engenharia e Pesquisa Aplicada
instname:Universidade Federal de Pernambuco (UFPE)
instacron:UFPE
instname_str Universidade Federal de Pernambuco (UFPE)
instacron_str UFPE
institution UFPE
reponame_str Revista de Engenharia e Pesquisa Aplicada
collection Revista de Engenharia e Pesquisa Aplicada
repository.name.fl_str_mv Revista de Engenharia e Pesquisa Aplicada - Universidade Federal de Pernambuco (UFPE)
repository.mail.fl_str_mv ||repa@poli.br
_version_ 1798036000421707776

Registros relacionados