scholarly journals EVALUATING MBSE POTENTIAL USING PRODUCT AND DEVELOPMENT CHARACTERISTICS – A STATISTICAL INVESTIGATION

2020 ◽  
Vol 1 ◽  
pp. 2385-2394
Author(s):  
M. Schöberl ◽  
E. Rebentisch ◽  
J. Trauer ◽  
M. Mörtl ◽  
J. Fottner
Keyword(s):  
Principal Component Analysis ◽  
Systems Engineering ◽  
Principal Component ◽  
Component Analysis ◽  
Statistical Investigation ◽  
Product Complexity ◽  
Organizational Complexity ◽  
Model Based ◽  
Using Data ◽  
Model Based Systems Engineering

AbstractAs model-based systems engineering (MBSE) is evolving, the need for evaluating MBSE approaches grows. Literature shows that there is an untested assertion in the MBSE community that complexity drives the adoption of MBSE. To assess this assertion and support the evaluation of MBSE, a principal component analysis was carried out on eight product and development characteristics using data collected in an MBSE course, resulting in organizational complexity, product complexity and inertia. To conclude, the method developed in this paper enables organisations to evaluate their MBSE adoption potential.

scholarly journals 4D Cone-beam CT reconstruction using a motion model based on principal component analysis

2011 ◽  
Vol 38 (12) ◽  
pp. 6697-6709 ◽  
Author(s):  
David Staub ◽  
Alen Docef ◽  
Robert S. Brock ◽  
Constantin Vaman ◽  
Martin J. Murphy
Keyword(s):  
Principal Component Analysis ◽  
Cone Beam Ct ◽  
Principal Component ◽  
Component Analysis ◽  
Cone Beam ◽  
Motion Model ◽  
Ct Reconstruction ◽  
Model Based

scholarly journals Application of Characteristic Model-Based Principal Component Analysis in Optimization of Flowmeter Parameters

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Wenping Jiang ◽  
Zhencun Jiang ◽  
Lingyang Wang ◽  
Jun Min ◽  
Yi Zhu ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Measurement Error ◽  
Measurement Accuracy ◽  
Structural Parameters ◽  
Principal Component ◽  
Component Analysis ◽  
Characteristic Model ◽  
Industrial Systems ◽  
Model Based ◽  
High Level

In complex industrial processes, it is necessary to perform modeling analysis on some industrial systems and find and optimize the factors that have the greatest impact on the results, in order to achieve the optimization of the industrial systems. However, due to the high-level nature or complex working mechanism of complex industrial systems, traditional principal component analysis methods are difficult to apply. Therefore, this paper proposes a characteristic model-based principal component analysis (CMPCA) to perform principal component analysis on complex industrial systems. The differential pressure flowmeter is taken as an example to verify the effectiveness of the method. Flowmeter is an indispensable instrument in measurement, and its accuracy depends on its own structural parameters. However, the measurement accuracy of some flow meters is not high, and the measurement error is large, which affects the normal industrial production process. This method is used to analyze the influence of the structural parameters of the flowmeter on its measurement accuracy, and the four most important structural parameters are found and optimized. The measurement error of the Bitoba flowmeter is reduced from 1% to 0.2%, and the measurement repeatability is reduced from 0.3 to 0.06, which proves the effectiveness of the method.

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