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An advanced cost estimation methodology for engineering systems

机译:工程系统的高级成本估算方法

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摘要

A mathematically advanced method for improving the fidelity of cost estimation for an engineering system is presented. In this method historical cost records can be expanded either through the use of local metamodels or by using an engineering build‐up model. In either case, the expanded data set is analyzed using principal component analysis (PCA) in order to identify the physical parameters, and the principal components (PCs) which demonstrate the highest correlation to the cost. A set of predictor variables, composed of the physical parameters and of the multipliers of the principal components which demonstrate the highest correlation to the cost, is developed. This new set of predictor variables is regressed, using the Kriging method, thus creating a cost estimation model with a high level of predictive capability and fidelity. The new methodology is used for analyzing a set of cost data available in the literature, and the new cost model is compared to results from a neural network based analysis and to a cost regression model. Further, a case study addressing the fabrication of a submarine pressure hull is developed in order to illustrate the new method. The results from the final regression model are presented and compared to results from other cost regression methods. The technical characteristics of the new novel general method are presented and discussed. © 2011 Wiley Periodicals, Inc. Syst Eng
机译:提出了一种数学上先进的方法来提高工程系统成本估算的逼真度。通过这种方法,可以通过使用本地元模型或使用工程构建模型来扩展历史成本记录。在这两种情况下,都使用主成分分析(PCA)来分析扩展后的数据集,以识别物理参数,以及与成本相关性最高的主成分(PC)。开发了一组预测变量,这些变量由物理参数和主要成分的乘数组成,这些变量显示出与成本的最高相关性。使用Kriging方法对这组新的预测变量进行回归,从而创建具有高水平预测能力和保真度的成本估算模型。新的方法用于分析文献中可用的一组成本数据,并将新的成本模型与基于神经网络的分析结果和成本回归模型进行比较。此外,为了说明这种新方法,还针对案例研究了潜艇压力船的制造。介绍了最终回归模型的结果,并将其与其他成本回归方法的结果进行了比较。介绍并讨论了新的新颖通用方法的技术特性。 ©2011 Wiley Periodicals,Inc.系统工程

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