基于无监督聚类和频繁子图挖掘的电力通信网缺陷诊断与自动派单

doi:10.11959/j.issn.1000-0801.2021253

Abstract

Abstract:

Fault diagnosis is one of the most challenging tasks in power communication.The fault diagnosis based on rules can no longer meet the demand of massive alarms processing.The existing approaches based on the supervised learning need large sets of the labeled data and sufficient time to train models for processing continuous data instead of alarms, which are far behind the feasibility of deployment.As for alarm correlation and fault pattern discovery, a self-learning algorithm based on the density-based clustering and frequent subgraph mining was proposed.A novel approach for automatic fault diagnosis and dispatch were also introduced, which provided the scalable and self-renewing ability and had been deployed to the automatic fault dispatch system.Experiments in the real-world datasets authorized the effectiveness for timely fault discovery and targeted fault dispatch.

Key words: power communication, fault diagnosis, unsupervised clustering, frequent subgraph mining

CLC Number:

TP393

Jihua WU, Pengyu ZHU, Zichen WU, Bin GU, Tao HONG, Bo GUO, Jing WANG, Jingyu WANG. Fault diagnosis and auto dispatchin of power communication network based on unsupervised clustering and frequent subgraph mining[J]. Telecommunications Science, 2021, 37(11): 51-63.

Figures/Tables 8

方法	预设簇	自学习	h-score	c-score	v-score
规则匹配	×	×	0.99	0.97	0.98
k-means	√	√	0.96	0.93	0.94
$D B S C A N$	×	√	$0 . 99$	$0 . 99$	$0 . 99$

方法	集合数目	归并与缺陷
方法	集合数目	一对一	一对多	多对一	多对多
规则匹配	163	76.71%	7.53%	13.70%	2.05%
k-means	146	58.71%	30.32%	7.74%	3.23%
$D B S C A N$	$153$	$84 . 93 %$	$10 . 96 %$	$3 . 42 %$	$0 . 68 %$

References 18

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预测	真实
预测	card abnormal	fiber breaking	power abnormal	power interruption
card abnormal	28	0	0	0
fiber breaking	0	60	0	1
power abnormal	0	0	12	0
power interruption	0	2	0	13

Fault diagnosis and auto dispatchin of power communication network based on unsupervised clustering and frequent subgraph mining

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