Health Assessment for a Sensor Network With Data Loss Based on Belief Rule Base
As the complexity of a system increases, the use of sensor networks becomes more frequent and the network health management becomes more and more important. When sensor networks are applied to complex environments, they are influenced by the disturbance factors in engineering practice and observatio...
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oai:doaj.org-article:6f1329ff6eaa473b832713cf15e150272021-11-19T00:03:39ZHealth Assessment for a Sensor Network With Data Loss Based on Belief Rule Base2169-353610.1109/ACCESS.2020.3007899https://doaj.org/article/6f1329ff6eaa473b832713cf15e150272020-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9136657/https://doaj.org/toc/2169-3536As the complexity of a system increases, the use of sensor networks becomes more frequent and the network health management becomes more and more important. When sensor networks are applied to complex environments, they are influenced by the disturbance factors in engineering practice and observation data may be lost. This will decrease the accuracy of the health state assessment. Moreover, due to the disturbance factors and complexity of the system, observation data and system information cannot be adequately gathered. To deal with the above problems, a new health assessment model is developed based on belief rule base (BRB). The BRB model is one of the expert systems in which the quantitative data and qualitative knowledge can be aggregated simultaneously. In the new health assessment model for a sensor network, a new missing data compensation model based on BRB is constructed first, in which the historical data of the monitoring indicators are used. In addition, the expert knowledge for the historical working state of the sensor network is also applied in the constructed missing data compensation model. Then, based on the compensated data and the observation data of the sensor network, the health state can be estimated by the developed health assessment model based on BRB. Given the uncertainty of expert knowledge, the initial health assessment model cannot assess the health state of the sensor network in an actual working environment. Thus, in this paper, an optimization model is constructed based on the projection covariance matrix adaption evolution strategy (P-CMA-ES). To illustrate the effectiveness of the new proposed model, a practical case study of a sensor network in a laboratory environment is conducted.Shaohua LiJingying FengWei HeRuihua QiHe GuoIEEEarticleHealth assessmentexpert systembelief rule base (BRB)sensor networkElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 8, Pp 126347-126357 (2020) |
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Health assessment expert system belief rule base (BRB) sensor network Electrical engineering. Electronics. Nuclear engineering TK1-9971 |
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Health assessment expert system belief rule base (BRB) sensor network Electrical engineering. Electronics. Nuclear engineering TK1-9971 Shaohua Li Jingying Feng Wei He Ruihua Qi He Guo Health Assessment for a Sensor Network With Data Loss Based on Belief Rule Base |
description |
As the complexity of a system increases, the use of sensor networks becomes more frequent and the network health management becomes more and more important. When sensor networks are applied to complex environments, they are influenced by the disturbance factors in engineering practice and observation data may be lost. This will decrease the accuracy of the health state assessment. Moreover, due to the disturbance factors and complexity of the system, observation data and system information cannot be adequately gathered. To deal with the above problems, a new health assessment model is developed based on belief rule base (BRB). The BRB model is one of the expert systems in which the quantitative data and qualitative knowledge can be aggregated simultaneously. In the new health assessment model for a sensor network, a new missing data compensation model based on BRB is constructed first, in which the historical data of the monitoring indicators are used. In addition, the expert knowledge for the historical working state of the sensor network is also applied in the constructed missing data compensation model. Then, based on the compensated data and the observation data of the sensor network, the health state can be estimated by the developed health assessment model based on BRB. Given the uncertainty of expert knowledge, the initial health assessment model cannot assess the health state of the sensor network in an actual working environment. Thus, in this paper, an optimization model is constructed based on the projection covariance matrix adaption evolution strategy (P-CMA-ES). To illustrate the effectiveness of the new proposed model, a practical case study of a sensor network in a laboratory environment is conducted. |
format |
article |
author |
Shaohua Li Jingying Feng Wei He Ruihua Qi He Guo |
author_facet |
Shaohua Li Jingying Feng Wei He Ruihua Qi He Guo |
author_sort |
Shaohua Li |
title |
Health Assessment for a Sensor Network With Data Loss Based on Belief Rule Base |
title_short |
Health Assessment for a Sensor Network With Data Loss Based on Belief Rule Base |
title_full |
Health Assessment for a Sensor Network With Data Loss Based on Belief Rule Base |
title_fullStr |
Health Assessment for a Sensor Network With Data Loss Based on Belief Rule Base |
title_full_unstemmed |
Health Assessment for a Sensor Network With Data Loss Based on Belief Rule Base |
title_sort |
health assessment for a sensor network with data loss based on belief rule base |
publisher |
IEEE |
publishDate |
2020 |
url |
https://doaj.org/article/6f1329ff6eaa473b832713cf15e15027 |
work_keys_str_mv |
AT shaohuali healthassessmentforasensornetworkwithdatalossbasedonbeliefrulebase AT jingyingfeng healthassessmentforasensornetworkwithdatalossbasedonbeliefrulebase AT weihe healthassessmentforasensornetworkwithdatalossbasedonbeliefrulebase AT ruihuaqi healthassessmentforasensornetworkwithdatalossbasedonbeliefrulebase AT heguo healthassessmentforasensornetworkwithdatalossbasedonbeliefrulebase |
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1718420673878032384 |