CAJ | 학술논문

为了提高故障定位性能，降低单一判别参数在单位过程中的约束，该文提出一种基于压缩感知和信息熵差的多参数链路故障定位算法。该算法首先利用贝叶斯网络进行快速故障预测，其次引入参数故障覆盖范围，利用压缩感知进行故障筛选，最后定义参数故障信息熵差完成根源故障定位。仿真结果表明，该算法预测出的故障集合具有可压缩性，筛选后的故障集合保留了真实故障，定位时具有较高的故障检测率和较低的故障误检率。
위료제고고장정위성능，강저단일판별삼수재단위과정중적약속，해문제출일충기우압축감지화신식적차적다삼수련로고장정위산법。해산법수선이용패협사망락진행쾌속고장예측，기차인입삼수고장복개범위，이용압축감지진행고장사선，최후정의삼수고장신식적차완성근원고장정위。방진결과표명，해산법예측출적고장집합구유가압축성，사선후적고장집합보류료진실고장，정위시구유교고적고장검측솔화교저적고장오검솔。
To improve the performance and decrease the constraints of fault localization with single distinguish parameter, a multi-parameters link failure localization algorithm is proposed based on compressive sensing and entropy difference. Firstly it makes a fast fault prediction by Bayesian network, then it introduces a parameter named fault coverage and selects probable link failure using compressive sensing, finally defines fault information entropy difference and obtains the root fault based on the parameter. The simulation results show that the predicted fault set can be compressed and the selected probable fault set contains the true fault, meanwhile the proposed algorithm achieves high detection rate and low false positive rate.