A wireless sensor network node fault diagnosis model based on belief rule base with power set
Wireless sensor network (WSN) is inevitably subject to node failures due to their harsh operating environments and extra-long working hours. In order to ensure reliable and correct data collection, WSN node fault diagnosis is necessary. Fault diagnosis of sensor nodes usually requires the extraction...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-10-01
|
| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844022021673 |
| _version_ | 1797991813697503232 |
|---|---|
| author | Guo-Wen Sun Wei He Hai-Long Zhu Zi-Jiang Yang Quan-Qi Mu Yu-He Wang |
| author_facet | Guo-Wen Sun Wei He Hai-Long Zhu Zi-Jiang Yang Quan-Qi Mu Yu-He Wang |
| author_sort | Guo-Wen Sun |
| collection | DOAJ |
| description | Wireless sensor network (WSN) is inevitably subject to node failures due to their harsh operating environments and extra-long working hours. In order to ensure reliable and correct data collection, WSN node fault diagnosis is necessary. Fault diagnosis of sensor nodes usually requires the extraction of data features from the original collected data. However, the data features of different types of faults sometimes have similarities, making it difficult to distinguish and represent the types of faults in the diagnosis results, these indistinguishable types of faults are called ambiguous information. Therefore, a belief rule base with power set (PBRB) fault diagnosis method is proposed. In this method, the power set identification framework is used to represent the fuzzy information, the evidential reasoning (ER) method is used as the reasoning process, and the projection covariance matrix adaptive evolution strategy (P-CMA-ES) is used as the parameter optimization algorithm. The results of the case study show that PBRB method has higher accuracy and better stability compared to other commonly used fault diagnosis methods. According to the research results, PBRB can not only represent the fault types that are difficult to distinguish, but also has the advantage of small sample training. This makes the model obtain high fault diagnosis accuracy and stability. |
| first_indexed | 2024-04-11T08:57:01Z |
| format | Article |
| id | doaj.art-36750c09bbde44a5a07b857f4f3434ac |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-11T08:57:01Z |
| publishDate | 2022-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-36750c09bbde44a5a07b857f4f3434ac2022-12-22T04:33:09ZengElsevierHeliyon2405-84402022-10-01810e10879A wireless sensor network node fault diagnosis model based on belief rule base with power setGuo-Wen Sun0Wei He1Hai-Long Zhu2Zi-Jiang Yang3Quan-Qi Mu4Yu-He Wang5Harbin Normal University, Harbin, 150025, ChinaHarbin Normal University, Harbin, 150025, China; Rocket Force University of Engineering, Xi'an 710025, China; Corresponding author.Harbin Normal University, Harbin, 150025, ChinaHeilongjiang Agricultural Engineering Vocational College, Harbin, 157041, ChinaHarbin Normal University, Harbin, 150025, ChinaHarbin Normal University, Harbin, 150025, ChinaWireless sensor network (WSN) is inevitably subject to node failures due to their harsh operating environments and extra-long working hours. In order to ensure reliable and correct data collection, WSN node fault diagnosis is necessary. Fault diagnosis of sensor nodes usually requires the extraction of data features from the original collected data. However, the data features of different types of faults sometimes have similarities, making it difficult to distinguish and represent the types of faults in the diagnosis results, these indistinguishable types of faults are called ambiguous information. Therefore, a belief rule base with power set (PBRB) fault diagnosis method is proposed. In this method, the power set identification framework is used to represent the fuzzy information, the evidential reasoning (ER) method is used as the reasoning process, and the projection covariance matrix adaptive evolution strategy (P-CMA-ES) is used as the parameter optimization algorithm. The results of the case study show that PBRB method has higher accuracy and better stability compared to other commonly used fault diagnosis methods. According to the research results, PBRB can not only represent the fault types that are difficult to distinguish, but also has the advantage of small sample training. This makes the model obtain high fault diagnosis accuracy and stability.http://www.sciencedirect.com/science/article/pii/S2405844022021673Fault diagnosisWireless sensor networkBelief rule basePower set |
| spellingShingle | Guo-Wen Sun Wei He Hai-Long Zhu Zi-Jiang Yang Quan-Qi Mu Yu-He Wang A wireless sensor network node fault diagnosis model based on belief rule base with power set Heliyon Fault diagnosis Wireless sensor network Belief rule base Power set |
| title | A wireless sensor network node fault diagnosis model based on belief rule base with power set |
| title_full | A wireless sensor network node fault diagnosis model based on belief rule base with power set |
| title_fullStr | A wireless sensor network node fault diagnosis model based on belief rule base with power set |
| title_full_unstemmed | A wireless sensor network node fault diagnosis model based on belief rule base with power set |
| title_short | A wireless sensor network node fault diagnosis model based on belief rule base with power set |
| title_sort | wireless sensor network node fault diagnosis model based on belief rule base with power set |
| topic | Fault diagnosis Wireless sensor network Belief rule base Power set |
| url | http://www.sciencedirect.com/science/article/pii/S2405844022021673 |
| work_keys_str_mv | AT guowensun awirelesssensornetworknodefaultdiagnosismodelbasedonbeliefrulebasewithpowerset AT weihe awirelesssensornetworknodefaultdiagnosismodelbasedonbeliefrulebasewithpowerset AT hailongzhu awirelesssensornetworknodefaultdiagnosismodelbasedonbeliefrulebasewithpowerset AT zijiangyang awirelesssensornetworknodefaultdiagnosismodelbasedonbeliefrulebasewithpowerset AT quanqimu awirelesssensornetworknodefaultdiagnosismodelbasedonbeliefrulebasewithpowerset AT yuhewang awirelesssensornetworknodefaultdiagnosismodelbasedonbeliefrulebasewithpowerset AT guowensun wirelesssensornetworknodefaultdiagnosismodelbasedonbeliefrulebasewithpowerset AT weihe wirelesssensornetworknodefaultdiagnosismodelbasedonbeliefrulebasewithpowerset AT hailongzhu wirelesssensornetworknodefaultdiagnosismodelbasedonbeliefrulebasewithpowerset AT zijiangyang wirelesssensornetworknodefaultdiagnosismodelbasedonbeliefrulebasewithpowerset AT quanqimu wirelesssensornetworknodefaultdiagnosismodelbasedonbeliefrulebasewithpowerset AT yuhewang wirelesssensornetworknodefaultdiagnosismodelbasedonbeliefrulebasewithpowerset |