A fault diagnosis method for wireless sensor network nodes based on a belief rule base with adaptive attribute weights

被引:7
作者
Shi, Ke-Xin [1 ]
Li, Shi-Ming [1 ]
Sun, Guo-Wen [1 ]
Feng, Zhi-Chao [2 ]
He, Wei [1 ]
机构
[1] Harbin Normal Univ, Harbin 150025, Peoples R China
[2] Rocket Force Univ Engn, Xian 710025, Peoples R China
关键词
Wireless sensor network; Fault diagnosis; Belief rule base; Adaptive attribute weights; MODEL;
D O I
10.1038/s41598-024-54589-6
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Due to the harsh operating environment and ultralong operating hours of wireless sensor networks (WSNs), node failures are inevitable. Ensuring the reliability of the data collected by the WSN necessitates the utmost importance of diagnosing faults in nodes within the WSN. Typically, the initial step in the fault diagnosis of WSN nodes involves extracting numerical features from neighboring nodes. A solitary data feature is often assigned a high weight, resulting in the failure to effectively distinguish between all types of faults. Therefore, this study introduces an enhanced variant of the traditional belief rule base (BRB), called the belief rule base with adaptive attribute weights (BRB-AAW). First, the data features are extracted as input attributes for the model. Second, a fault diagnosis model for WSN nodes, incorporating BRB-AAW, is established by integrating parameters initialized by expert knowledge with the extracted data features. Third, to optimize the model's initial parameters, the projection covariance matrix adaptive evolution strategy (P-CMA-ES) algorithm is employed. Finally, a comprehensive case study is designed to verify the accuracy and effectiveness of the proposed method. The results of the case study indicate that compared with the traditional BRB method, the accuracy of the proposed model in WSN node fault diagnosis is significantly improved.
引用
收藏
页数:20
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