scholarly journals Target Detection and Classification From Sub-optimal Experimental Data Using Principal Component Analysis and Feature Vector Masking

2016 ◽  
Vol 6 (2) ◽  
pp. 110-118
Author(s):  
Everett R. Coots ◽  
◽  
William W. Arrasmith ◽  
Eric A. Skowbo ◽  
John V. Olson ◽  
...  
Keyword(s):  
Experimental Data ◽  
Principal Component Analysis ◽  
Target Detection ◽  
Feature Vector ◽  
Principal Component ◽  
Component Analysis

scholarly journals Low-temperature thermal hydrolysis of sludge prior to anaerobic digestion: principal component analysis (PCA) of experimental data

Data in Brief ◽  
2021 ◽  
pp. 107323
Author(s):  
Mohamed N.A. Meshref ◽  
Seyed Mohammad Mirsoleimani Azizi ◽  
Wafa Dastyar ◽  
Rasha Maal-Bared ◽  
Bipro Ranjan Dhar
Keyword(s):  
Experimental Data ◽  
Principal Component Analysis ◽  
Anaerobic Digestion ◽  
Low Temperature ◽  
Principal Component ◽  
Component Analysis ◽  
Thermal Hydrolysis ◽  
Hydrolysis Of

Characterization of fretting wear experiments on spline couplings by principal component analysis

2016 ◽  
Vol 231 (7) ◽  
pp. 860-868 ◽  
Author(s):  
Waqar Qureshi ◽  
Francesca Cura ◽  
Andrea Mura
Keyword(s):  
Experimental Data ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Fretting Wear ◽  
Aircraft Industry ◽  
Surface Movement ◽  
Noisy Measurements ◽  
Quasi Static Process

Fretting wear is a quasi-static process in which repeated relative surface movement of components results in wear and fatigue. Fretting wear is quite significant in the case of spline couplings which are frequently used in the aircraft industry to transfer torque and power. Fretting wear depends on materials, pressure distribution, torque, rotational speeds, lubrication, surface finish, misalignment between spline shafts, etc. The presence of so many factors makes it difficult to conduct experiments for better models of fretting wear and it is the case whenever a mathematical model is sought from experimental data which is prone to noisy measurements, outliers and redundant variables. This work develops a principal component analysis based method, using a criterion which is insensitive to outliers, to realize a better design and interpret experiments on fretting wear. The proposed method can be extended to other cases too.

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