Hydraulic metal structure health diagnosis based on data mining technology  

作　　者：Guang-ming Yang Xiao Feng Kun Yang 

机构地区：[1]College of Energy and Electrical Engineering, Hohai University [2]Research Center for Renewable Energy Generation Engineering, Ministry of Education, Hohai University [3]Dayu College, Hohai University

出　　处：《Water Science and Engineering》2015年第2期158-163,共6页水科学与水工程（英文版）

基　　金：supported by the Key Program of the National Natural Science Foundation of China(Grant No.50539010);the Special Fund for Public Welfare Industry of the Ministry of Water Resources of China(Grant No.200801019)

摘　　要：In conjunction with association rules for data mining, the connections between testing indices and strong and weak association rules were determined, and new derivative rules were obtained by further reasoning. Association rules were used to analyze correlation and check consistency between indices. This study shows that the judgment obtained by weak association rules or non-association rules is more accurate and more credible than that obtained by strong association rules. When the testing grades of two indices in the weak association rules are inconsistent, the testing grades of indices are more likely to be erroneous, and the mistakes are often caused by human factors. Clustering data mining technology was used to analyze the reliability of a diagnosis, or to perform health diagnosis directly. Analysis showed that the clustering results are related to the indices selected, and that if the indices selected are more significant, the characteristics of clustering results are also more significant, and the analysis or diagnosis is more credible. The indices and diagnosis analysis function produced by this study provide a necessary theoretical foundation and new ideas for the development of hydraulic metal structure health diagnosis technology.In conjunction with association rules for data mining, the connections between testing indices and strong and weak association rules were determined, and new derivative rules were obtained by further reasoning. Association rules were used to analyze correlation and check consistency between indices. This study shows that the judgment obtained by weak association rules or non-association rules is more accurate and more credible than that obtained by strong association rules. When the testing grades of two indices in the weak association rules are inconsistent, the testing grades of indices are more likely to be erroneous, and the mistakes are often caused by human factors. Clustering data mining technology was used to analyze the reliability of a diagnosis, or to perform health diagnosis directly. Analysis showed that the clustering results are related to the indices selected, and that if the indices selected are more significant, the characteristics of clustering results are also more significant, and the analysis or diagnosis is more credible. The indices and diagnosis analysis function produced by this study provide a necessary theoretical foundation and new ideas for the development of hydraulic metal structure health diagnosis technology.

关 键 词：Hydraulic metal structure Health diagnosis Data mining technology Clustering model Association rule 

分 类 号：TV34[水利工程—水工结构工程]