QUALITY EVALUATION USING GEOMETRIC DISTANCE APPROACH

1999 ◽  
Vol 06 (02) ◽  
pp. 139-153 ◽  
Author(s):  
FU-KWUN WANG ◽  
NORMA F. HUBELE
Keyword(s):  
Control Variable ◽  
Real Data ◽  
Statistical Technique ◽  
Quality Characteristics ◽  
Process Data ◽  
Multivariate Statistical ◽  
Geometric Distance ◽  
Multivariate Quality Control ◽  
Performance Metric ◽  
Multivariate Control

Quantitatively assessing quality and using this assessment for competitive benchmarking and diagnostics of manufactured part failure are very important for continuous improvement in modern manufacturing industries. Process capability analysis often entails characterizing or assessing process specification or quality characteristics. When these quality characteristics are related, the analysis should be based on a multivariate statistical technique. A current problem in multivariate quality control is that there is no consensus about a methodology for assessing capability. Thus, the critical first step in instituting a multivariate control scheme is not well defined. While numerous authors have recently proposed alternative definitions of multivariate capability indices, those methods may not be practical in some cases. In this research, a new process control variable, geometric distance (GD), for assessing or evaluating the quality of manufactured product is developed and investigated for reducing dimensionality. The theoretical distribution of the geometric distance is investigated and a suitable performance metric of the multivariate process data is proposed. Finally, some real data are used to demonstrate the capability of the proposed method.

scholarly journals The Ranking Function for Solving the Fuzzy Hotelling T2 Test

2021 ◽  
pp. 2653-2659
Author(s):  
Esraa Dhafer Thamer ◽  
Iden Hasan Hussein
Keyword(s):  
Control Chart ◽  
Real Data ◽  
Quality Characteristics ◽  
Ranking Function ◽  
Multivariate Quality Control ◽  
Multivariate Control Chart ◽  
Quality Control Chart ◽  
Triangular Membership Function ◽  
Multivariate Control

     A multivariate control chart is measured by many variables that are correlated in production, using the quality characteristics in any product. In this paper, statistical procedures were employed to find the multivariate quality control chart by utilizing fuzzy Hotelling  test. The procedure utilizes the triangular membership function to treat the real data, which were collected from Baghdad Soft Drinks Company in Iraq. The quality of production was evaluated by using a new method of the ranking function.

scholarly journals Oversizing Thread Diagnosis in Tapping Operation

Metals ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 537
Author(s):  
Alain Gil Del Val ◽  
Fernando Veiga ◽  
Mariluz Penalva ◽  
Miguel Arizmendi
Keyword(s):  
Control Chart ◽  
Manufacturing Systems ◽  
Spindle Motor ◽  
Multivariate Statistical ◽  
Statistical Process ◽  
Knowing That ◽  
On Line ◽  
High Level ◽  
Screw Threads ◽  
Multivariate Control

Automotive, railway and aerospace sectors require a high level of quality on the thread profiles in their manufacturing systems knowing that the tapping process is a complex manufacturing process and the last operation in a manufacturing cell. Therefore, a multivariate statistical process control chart, for each tap, is presented based on the principal components of the torque signal directly measured from spindle motor drive to diagnosis the thread profile quality. This on-line multivariate control chart has implemented an alarm to avoid defected screw threads (oversized). Therefore, it could work automatically without any operator intervention assessing the thread quality and the safety is guaranteed during the tapping process.

scholarly journals Geostatistical Learning: Challenges and Opportunities

2021 ◽  
Vol 7 ◽  
Author(s):  
Júlio Hoffimann ◽  
Maciel Zortea ◽  
Breno de Carvalho ◽  
Bianca Zadrozny
Keyword(s):  
Statistical Learning ◽  
Spatial Correlation ◽  
Language Processing ◽  
Real Data ◽  
Model Errors ◽  
Process Data ◽  
Applied Machine Learning ◽  
Geophysical Surveys ◽  
Challenges And Opportunities ◽  
Geophysical Processes

Statistical learning theory provides the foundation to applied machine learning, and its various successful applications in computer vision, natural language processing and other scientific domains. The theory, however, does not take into account the unique challenges of performing statistical learning in geospatial settings. For instance, it is well known that model errors cannot be assumed to be independent and identically distributed in geospatial (a.k.a. regionalized) variables due to spatial correlation; and trends caused by geophysical processes lead to covariate shifts between the domain where the model was trained and the domain where it will be applied, which in turn harm the use of classical learning methodologies that rely on random samples of the data. In this work, we introduce the geostatistical (transfer) learning problem, and illustrate the challenges of learning from geospatial data by assessing widely-used methods for estimating generalization error of learning models, under covariate shift and spatial correlation. Experiments with synthetic Gaussian process data as well as with real data from geophysical surveys in New Zealand indicate that none of the methods are adequate for model selection in a geospatial context. We provide general guidelines regarding the choice of these methods in practice while new methods are being actively researched.

scholarly journals Mixture Based Outlier Filtration

10.14311/816 ◽  
2006 ◽  
Vol 46 (2) ◽  
Author(s):  
P. Pecherková ◽  
I. Nagy
Keyword(s):  
Model Identification ◽  
Real Data ◽  
Control Algorithms ◽  
Linear Quadratic ◽  
Process Data ◽  
Linear Quadratic Gaussian ◽  
Standard Methods ◽  
Two Component ◽  
Reconstruction Filter

Success/failure of adaptive control algorithms – especially those designed using the Linear Quadratic Gaussian criterion – depends on the quality of the process data used for model identification. One of the most harmful types of process data corruptions are outliers, i.e. ‘wrong data’ lying far away from the range of real data. The presence of outliers in the data negatively affects an estimation of the dynamics of the system. This effect is magnified when the outliers are grouped into blocks. In this paper, we propose an algorithm for outlier detection and removal. It is based on modelling the corrupted data by a two-component probabilistic mixture. The first component of the mixture models uncorrupted process data, while the second models outliers. When the outlier component is detected to be active, a prediction from the uncorrupted data component is computed and used as a reconstruction of the observed data. The resulting reconstruction filter is compared to standard methods on simulated and real data. The filter exhibits excellent properties, especially in the case of blocks of outliers. 

Exploring competitiveness and wellbeing in Italy by spatial principal component analysis

2021 ◽  
pp. 141-146
Author(s):  
Carlo Cusatelli ◽  
Massimiliano Giacalone ◽  
Eugenia Nissi
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Well Being ◽  
Component Analysis ◽  
Measurement Model ◽  
Statistical Technique ◽  
Multivariate Statistical ◽  
Spatial Principal Component Analysis ◽  
Italian Provinces ◽  
Reflective Measurement

Well being is a multidimensional phenomenon, that cannot be measured by a single descriptive indicator and that, it should be represented by multiple dimensions. It requires, to be measured by combination of different dimensions that can be considered together as components of the phenomenon. This combination can be obtained by applying methodologies knows as Composite Indicators (CIs). CIs are largely used to have a comprehensive view on a phenomenon that cannot be captured by a single indicator. Principal Component Analysis (PCA) is one of the most popular multivariate statistical technique used for reducing data with many dimension, and often well being indicators are obtained using PCA. PCA is implicitly based on a reflective measurement model that it non suitable for all types of indicators. Mazziotta and Pareto (2013) in their paper discuss the use and misuse of PCA for measuring well-being. The classical PCA is not suitable for data collected on the territory because it does not take into account the spatial autocorrelation present in the data. The aim of this paper is to propose the use of Spatial Principal Component Analysis for measuring well being in the Italian Provinces.

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