Fixture Failure Diagnosis for Autobody Assembly Using Pattern Recognition

1996 ◽  
Vol 118 (1) ◽  
pp. 55-66 ◽  
Author(s):  
D. Ceglarek ◽  
J. Shi
Keyword(s):  
Principal Component Analysis ◽  
Pattern Recognition ◽  
Diagnostic Method ◽  
Measurement Data ◽  
Principal Component ◽  
Failure Diagnosis ◽  
Variation Pattern ◽  
Mapping Procedure ◽  
Pattern Recognition Approach

In this paper, a fault diagnostic method is proposed for autobody assembly fixtures. This method uses measurement data to detect and isolate dimensional faults of part caused by fixture. The proposed method includes a predetermined variation pattern model and a fault mapping procedure. The variation pattern model is based on CAD information about the fixture geometry and location of the measurement points. This fault mapping procedure combines Principal Component Analysis with pattern recognition approach. Simulations and one case study illustrate the proposed method.

Dimensional Fault Diagnosis for Compliant Beam Structure Assemblies

1999 ◽  
Vol 122 (4) ◽  
pp. 773-780 ◽  
Author(s):  
Q. Rong ◽  
D. Ceglarek ◽  
J. Shi
Keyword(s):  
Principal Component Analysis ◽  
Fault Diagnosis ◽  
Stiffness Matrix ◽  
Measurement Data ◽  
Principal Component ◽  
Statistical Hypothesis ◽  
Beam Structure ◽  
Mapping Procedure ◽  
Aerospace Assembly

A model-based diagnostic methodology is proposed for the dimensional fault diagnosis of compliant beam structures in automotive or aerospace assembly processes. In the diagnosis procedure, the product measurement data are used to detect and isolate dimensional faults caused by part fabrication error in compliant beam assemblies. The proposed method includes a predetermined fault patterns model and a fault mapping procedure. The fault patterns are modeled by the diagnostic vectors derived from the inversed stiffness matrix of the beam structure. The fault mapping procedure combines principal component analysis (PCA) of measurement data and fault pattern recognition using statistical hypothesis tests. Verification of the proposed method is presented through simulations and one case study. [S1087-1357(00)02502-8]

The Heartbeat Classification Based on PCA

2012 ◽  
Vol 195-196 ◽  
pp. 402-406
Author(s):  
Xue Qin Chen ◽  
Rui Ping Wang
Keyword(s):  
Principal Component Analysis ◽  
Pattern Recognition ◽  
Recognition Task ◽  
Principal Component ◽  
True Positive Rate ◽  
True Positive ◽  
Heartbeat Classification ◽  
Positive Rate ◽  
Electrocardiogram Ecg ◽  
Pattern Recognition Task

Classify the electrocardiogram (ECG) into different pathophysiological categories is a complex pattern recognition task which has been tried in lots of methods. This paper will discuss a method of principal component analysis (PCA) in exacting the heartbeat features, and a new method of classification that is to calculate the error between the testing heartbeat and reconstructed heartbeat. Training and testing heartbeat is taken from the MIT-BIH Arrhythmia Database, in which 8 types of arrhythmia signals are selected in this paper. The true positive rate (TPR) is 83%.

Application of the principal component analysis on geochemical data: a case study in the basement complex of Southern Ilesa area, Nigeria

2010 ◽  
Vol 4 (1-2) ◽  
pp. 239-247 ◽  
Author(s):  
Emmanuel A. Ariyibi ◽  
Samuel L. Folami ◽  
Bankole D. Ako ◽  
Taye R. Ajayi ◽  
Adebowale O. Adelusi
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Geochemical Data ◽  
Basement Complex

Scope and limitations of principal component analysis of high resolution LC-TOF-MS data: the analysis of the chlorogenic acid fraction in green coffee beans as a case study

2011 ◽  
Vol 3 (1) ◽  
pp. 144-155 ◽  
Author(s):  
Nikolai Kuhnert ◽  
Rakesh Jaiswal ◽  
Pinkie Eravuchira ◽  
Rasha M. El-Abassy ◽  
Bernd von der Kammer ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
High Resolution ◽  
Chlorogenic Acid ◽  
Principal Component ◽  
Green Coffee ◽  
Acid Fraction ◽  
Green Coffee Beans ◽  
Coffee Beans ◽  
Tof Ms
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