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. Associ...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.展开更多
The multi-scale characteristics of clusters in a fast fluidized bed and of agglomerates in a fluidized bed of cohesive particles are discussed on the basis of large amounts of experiments. The cluster size and concent...The multi-scale characteristics of clusters in a fast fluidized bed and of agglomerates in a fluidized bed of cohesive particles are discussed on the basis of large amounts of experiments. The cluster size and concentration are dominated by the local voidage of the bed. A cluster consists of many sub-clusters with different sizes and discrete par-ticles, and the sub-cluster size probability density distribution appears as a negative exponential function. The agglom-erates in a fluidized bed of cohesive particles also possess the multi-scale nature. The large agglomerates form a fixed bed at the bottom, the medium agglomerates are fluidized in the middle, and the small agglomerates and discrete parti-cles become the dilute-phase region in the upper part of the bed. The agglomerate size is mainly affected by cohesive forces and gas velocity. The present models for predicting the size of clusters and agglomerates can not tackle the in-trinsic mechanism of the multi-scale aggregation, and a challenging problem for establishing mechanistic model is put forward.展开更多
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)
文摘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.
文摘The multi-scale characteristics of clusters in a fast fluidized bed and of agglomerates in a fluidized bed of cohesive particles are discussed on the basis of large amounts of experiments. The cluster size and concentration are dominated by the local voidage of the bed. A cluster consists of many sub-clusters with different sizes and discrete par-ticles, and the sub-cluster size probability density distribution appears as a negative exponential function. The agglom-erates in a fluidized bed of cohesive particles also possess the multi-scale nature. The large agglomerates form a fixed bed at the bottom, the medium agglomerates are fluidized in the middle, and the small agglomerates and discrete parti-cles become the dilute-phase region in the upper part of the bed. The agglomerate size is mainly affected by cohesive forces and gas velocity. The present models for predicting the size of clusters and agglomerates can not tackle the in-trinsic mechanism of the multi-scale aggregation, and a challenging problem for establishing mechanistic model is put forward.
