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دسته بندی:
داده های بزرگ - big data
سال انتشار:
2018
عنوان انگلیسی مقاله:
Improving early OSV design robustness by applying Multivariate Big Data Analytics on a ships life cycle
ترجمه فارسی عنوان مقاله:
بهبود استحکام طراحی اولیه OSV با استفاده از «تجزیه و تحلیل داده های چند متغیره» در یک چرخه عمر کشتی
منبع:
Sciencedirect - Elsevier - Journal of Industrial Information Integration,Corrected proof,doi:10:1016/j:jii:2018:02:002
نویسنده:
Niki Sadat Abbasiana,b,⁎, Afshin Salajegheha, Henrique Gasparb, Per Olaf Brettc
چکیده انگلیسی:
Typically, only a smaller portion of the monitorable operational data (e.g. from sensors and environment) from
Offshore Support Vessels (OSVs) are used at present. Operational data, in addition to equipment performance
data, design and construction data, creates large volumes of data with high veracity and variety. In most cases,
such data richness is not well understood as to how to utilize it better during design and operation. It is, very
often, too time consuming and resource demanding to estimate the final operational performance of vessel
concept design solution in early design by applying simulations and model tests. This paper argues that there is a
significant potential to integrate ship lifecycle data from different phases of its operation in large data repository
for deliberate aims and evaluations. It is disputed discretely in the paper, evaluating performance of real similar
type vessels during early stages of the design process, helps substantially improving and fine-tuning the per
formance criterion of the next generations of vessel design solutions. Producing learning from such a ship
lifecycle data repository to find useful patterns and relationships among design parameters and existing fleet real
performance data, requires the implementation of modern data mining techniques, such as big data and clus
tering concepts, which are introduced and applied in this paper. The analytics model introduced suggests and
reviews all relevant steps of data knowledge discovery, including pre-processing (integration, feature selection
and cleaning), processing (data analyzing) and post processing (evaluating and validating results) in this context.
Keywords: External data ، Internal data ، Abnormality ، Missing data ، Outliers ، Randomness ، Multivariate analysis ، Data integration ، Clustering
قیمت: رایگان
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