English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The urgency of demanding for large data in power industry will greatly exceed that of in other basic energy industries in the future. Through analyzing the power equipment condition monitoring data, we can assess more accurately the operating condition of power equipment, predict the life span of the equipment, and thus can effectively avoid economic losses caused by excessive supervision as well as shortness of supervision. To further promote the wide and deep application of power big data technology in condition monitoring, this paper summarizes equipment condition monitoring studies based on big data technology, and discusses applications of the key technologies of Big Data-Data Mining in condition monitoring. For data mining techniques, this paper uses a rough set theory to study application in knowledge and data discovery and fault monitoring. And for the presence of some problems of rough set theory applied in practical systems, some directions for further research are proposed.

Research on Condition Monitoring of Power Big Data Based on Rough Sets