CAJ | 학술논문

由于大规模无线传感器网络的动态拓扑性及资源受限，无线传感网的故障诊断成为该领域内的一个难点。现有的诊断方法消耗大量通信带宽和节点资源，给资源有限的网络带来繁重的负担。本文提出一种利用感知数据时域特征来检测故障以及对故障进行分类的被动诊断方法（TDSD）。首先运用一维离散Gabor变换对感知数据进行特征提取与分析，进而结合SOM神经网络对数据进行诊断与分类，判断当前网络状态并找出故障原因。实验结果表明，与其它方法相比，此方法具有网络通信负担小、诊断准确率高及分类效果好等优点，对节点故障和网络故障诊断都具有较高的诊断精度。
유우대규모무선전감기망락적동태탁복성급자원수한，무선전감망적고장진단성위해영역내적일개난점。현유적진단방법소모대량통신대관화절점자원，급자원유한적망락대래번중적부담。본문제출일충이용감지수거시역특정래검측고장이급대고장진행분류적피동진단방법（TDSD）。수선운용일유리산Gabor변환대감지수거진행특정제취여분석，진이결합SOM신경망락대수거진행진단여분류，판단당전망락상태병조출고장원인。실험결과표명，여기타방법상비，차방법구유망락통신부담소、진단준학솔고급분류효과호등우점，대절점고장화망락고장진단도구유교고적진단정도。
Due to the dynamic network topology and limit of resources,fault diagnosis for wireless sensor networks is difficult. The existing diagnostic methods consume large of communication bandwidth and node resources,which lead to heavy burden of the resources-limited network. This paper presents a passive diagnosis method used for fault detection and fault classification based on the time domain features of sensing data(TDSD). Firstly ,the feature ex?traction and analysis of the sensing data are carried out using one-dimensional discrete Gabor transform,and then the data are diagnosed and classified with SOM neural network,finally the current network status and identify the fault cause are determined. The results show that,comparing with other methods,this method has fewer burdens in network communication,better diagnostic accuracy rate and classification results,etc,and it has a high diagnostic accuracy especially for both node fault and network fault.