Sun Guo-Wen, He Wei, Zhu Hai-Long, Yang Zi-Jiang, Mu Quan-Qi, Wang Yu-He
Harbin Normal University, Harbin, 150025, China.
Rocket Force University of Engineering, Xi'an 710025, China.
Heliyon. 2022 Oct 7;8(10):e10879. doi: 10.1016/j.heliyon.2022.e10879. eCollection 2022 Oct.
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.
无线传感器网络（WSN）因其恶劣的运行环境和超长的工作时间，不可避免地会出现节点故障。为了确保可靠且正确的数据采集，WSN节点故障诊断是必要的。传感器节点的故障诊断通常需要从原始采集数据中提取数据特征。然而，不同类型故障的数据特征有时存在相似性，使得在诊断结果中难以区分和表示故障类型，这些难以区分的故障类型被称为模糊信息。因此，提出了一种带幂集的置信规则库（PBRB）故障诊断方法。在该方法中，幂集识别框架用于表示模糊信息，证据推理（ER）方法用作推理过程，投影协方差矩阵自适应进化策略（P-CMA-ES）用作参数优化算法。案例研究结果表明，与其他常用的故障诊断方法相比，PBRB方法具有更高的准确性和更好的稳定性。根据研究结果，PBRB不仅可以表示难以区分的故障类型，还具有小样本训练的优势。这使得模型获得了较高的故障诊断准确性和稳定性。
