基于RS-CSA-ELM的WSN节点故障诊断

为及时发现WSN节点故障隐患,准确掌握WSN运行状态,本文利用粗糙集理论属性约简算法(简称RS)对WSN节点故障属性进行约简,以最优的故障属性决策表重构训练样本数据集,作为极限学习机(Extreme Learning Machine, ELM)神经网络的输入,建立一个数据驱动的WSN节点故障断模型.采用乌鸦搜索算法(Crow Search Algorithm, CSA)优化ELM神经网络的输入权值和隐含层阀值,改善网络参数随机生成带来的ELM模型输出不稳定、分类精度偏低的问题.通过对RS-GA-ELM模型进行仿真分析.结果表明, RS-GA-ELM模型能够在可靠性不同的数据集中,保持较高的故障诊断效率,符合WSN节点故障诊断的需求.

Fault Diagnosis of WSN Nodes Based on RS-CSA-ELM

In order to discover the hidden troubles of WSN nodes in time and accurately know the running status of WSN, this paper uses the attribute reduction algorithm of rough set theory (RS for short) to reduce the fault attributes of WSN nodes, and reconstructs the training sample data set with the optimal fault attribute decision table as an input to the Extreme Learning Machine (ELM) neural network. In this way, a data-driven fault diagnosis model of WSN nodes is established. The input weights and hidden layer thresholds of the ELM neural network are optimized through Crow Search Algorithm (CSA) to alleviate the unstable output and improve the low classification accuracy of the ELM model caused by the random generation of network parameters. Simulation analysis of the RS-GA-ELM model is carried out. The results show that the RS-GA-ELM model can keep efficiently diagnose faults in data sets with different reliability, which meets the needs of fault diagnosis of WSN nodes.