Fault diagnosis and auto dispatchin of power communication network based on unsupervised clustering and frequent subgraph mining
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 f...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2021-11-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2021253/ |
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| _version_ | 1841528879472181248 |
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| author | Jihua WU Pengyu ZHU Zichen WU Bin GU Tao HONG Bo GUO Jing WANG Jingyu WANG |
| author_facet | Jihua WU Pengyu ZHU Zichen WU Bin GU Tao HONG Bo GUO Jing WANG Jingyu WANG |
| author_sort | Jihua WU |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ad45123b54ee4f4b9f793246bb1ff327 |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2021-11-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-ad45123b54ee4f4b9f793246bb1ff3272025-01-15T03:33:00ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012021-11-0137516359815887Fault diagnosis and auto dispatchin of power communication network based on unsupervised clustering and frequent subgraph miningJihua WUPengyu ZHUZichen WUBin GUTao HONGBo GUOJing WANGJingyu WANGFault 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.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2021253/power communicationfault diagnosisunsupervised clusteringfrequent subgraph mining |
| spellingShingle | 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 Dianxin kexue power communication fault diagnosis unsupervised clustering frequent subgraph mining |
| title | Fault diagnosis and auto dispatchin of power communication network based on unsupervised clustering and frequent subgraph mining |
| title_full | Fault diagnosis and auto dispatchin of power communication network based on unsupervised clustering and frequent subgraph mining |
| title_fullStr | Fault diagnosis and auto dispatchin of power communication network based on unsupervised clustering and frequent subgraph mining |
| title_full_unstemmed | Fault diagnosis and auto dispatchin of power communication network based on unsupervised clustering and frequent subgraph mining |
| title_short | Fault diagnosis and auto dispatchin of power communication network based on unsupervised clustering and frequent subgraph mining |
| title_sort | fault diagnosis and auto dispatchin of power communication network based on unsupervised clustering and frequent subgraph mining |
| topic | power communication fault diagnosis unsupervised clustering frequent subgraph mining |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2021253/ |
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