Paper Info
Title
Fault diagnosis of transformer using association rule mining and knowledge base
Abstract
Association rule mining makes interesting associations and/or correlations among large sets of data. Those associations can be refined as decision rules to be used and stored in a knowledge base system. In this paper, an approach based on association rule and knowledge base is proposed and implemented in the fault diagnosis of a transformer system. According to the features of association rule, the Apriori algorithm is adopted and modified to generate decision rules from power transformer information for building knowledge base, then the rules can be refined to diagnose the fault of the transformer through reasoning, and a prototype system is developed. This approach based on association rule is described in detail and the application is illustrated by an example. A comparison with the IEC (International Electrotechnical Commission) three-ratio method shows the proposed method can provide better accuracy in performance.
Year
DOI
Venue
2010
10.1109/ISDA.2010.5687177
ISDA
Keywords
Field
DocType
knowledge based systems,knowledge base,three-ratio method,apriori,inference mechanisms,prototype system,decision rules,power transformer,power transformer information,transformer fault diagnosis,reasoning,apriori algorithm,fault diagnosis,power engineering computing,association rule mining,knowledge base system,dissolved gas analysis,data mining,international electrotechnical commission,power transformers,decision rule,association rules,knowledge based system,association rule,cognition
Decision rule,Data mining,Dissolved gas analysis,Computer science,Apriori algorithm,A priori and a posteriori,Knowledge-based systems,Transformer,Association rule learning,Artificial intelligence,Knowledge base,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-4244-8134-7
1
0.35
References 
Authors
4
4
Name
Order
Citations
PageRank
Tiefeng Zhang121.05
Jie Lu2516.19
Guangquan Zhang3708.88
Qian Ding410.35