Hydraulic metal structure health diagnosis based on data mining 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.

基于PDCA的船舶设计质量管理

为提高船舶设计质量管理水平,以某满足《协调共同结构规范》(Harmonized Common Structure Rule,HCSR)的巴拿马型散货船为研究对象,利用计划、执行、检查、处理(Plan,Do,Check,Act,PDCA)循环法基本原则对设计质量进行管控,并建立同一船型设计质量管理体系。此外,对船舶能效指数(Energy Efficiency Design Index,EEDI)提升和结构轻量化设计的研讨方法进行分析。研究成果可为船舶设计质量管理提供一定参考。

Total plant performance evaluation based on big data: Visualization analysis of TE process

The performance evaluation of the process industry, which has been a popular topic nowadays, can not only find the weakness and verify the resilience and reliability of the process, but also provide some suggestions to improve the process benefits and efficiency. Nevertheless, the performance assessment principally concentrates upon some parts of the entire system at present, for example the controller assessment. Although some researches focus on the whole process, they aim at discovering the relationships between profit, society, policies and so forth, instead of relations between overall performance and some manipulated variables, that is, the total plant performance. According to the big data of different performance statuses, this paper proposes a hierarchical framework to select some structured logic rules from monitored variables to estimate the current state of the process. The variables related to safety and profits are regarded as key factors to performance evaluation. To better monitor the process state and observe the performance variation trend of the process, a classificationvisualization method based on kernel principal component analysis(KPCA) and self-organizing map(SOM) is established. The dimensions of big data produced by the process are first reduced by KPCA and then the processed data will be mapped into a two-dimensional grid chart by SOM to evaluate the performance status. The monitoring method is applied to the Tennessee Eastman process. Monitoring results indicate that off-line and on-line performance status can be well detected in a two-dimensional diagram.

Optimal Structural Design of the Midship of a VLCC Based on the Strategy Integrating SVM and GA

In this paper a hybrid process of modeling and optimization, which integrates a support vector machine （SVM） and genetic algorithm （GA）, was introduced to reduce the high time cost in structural optimization of ships. SVM, which is rooted in statistical learning theory and an approximate implementation of the method of structural risk minimization, can provide a good generalization performance in metamodeling the input-output relationship of real problems and consequently cuts down on high time cost in the analysis of real problems, such as FEM analysis. The GA, as a powerful optimization technique, possesses remarkable advantages for the problems that can hardly be optimized with common gradient-based optimization methods, which makes it suitable for optimizing models built by SVM. Based on the SVM-GA strategy, optimization of structural scantlings in the midship of a very large crude carrier （VLCC） ship was carried out according to the direct strength assessment method in common structural rules （CSR）, which eventually demonstrates the high efficiency of SVM-GA in optimizing the ship structural scantlings under heavy computational complexity. The time cost of this optimization with SVM-GA has been sharply reduced, many more loops have been processed within a small amount of time and the design has been improved remarkably.

Pruning Redundant Alarm Correlation Patterns

Efficient methods exist for discovering association rules fromlarge collections of data. The number of discovered rules can,however, be so large. At the same time it is well known that manydiscovered associations are redundant or minor variations of others.Their existence may simply be due to chance rather than truecorrelation. Thus, those spurious and insignificant rules should beremoved. In this paper, we propose a novel technique to over- Comethis problem. The technique firstly introduces the newconcept-structure rule cover, and then present a Quantitative methodto prune redundant correlation patterns. The user can now obtain acomplete picture of the do- Main without being overwhelmed by a hugenumber of rules.