Fault detection using Linear Discriminant Analysis with selection of process variables and time lags

2010 ◽  
Author(s):  
Anderson da Silva Soares ◽  
Roberto Kawakami Harrop Galvao
Keyword(s):  
Discriminant Analysis ◽  
Fault Detection ◽  
Linear Discriminant Analysis ◽  
Time Lags ◽  
Process Variables ◽  
Linear Discriminant ◽  
Selection Of

Comparison and selection of vegetation indices for detection of Sclerotinia Stem Rot on oilseed rape leaves using ground-based hyperspectral imaging

2017 ◽  
Vol 8 (2) ◽  
pp. 264-266 ◽  
Author(s):  
C. Zhang ◽  
F. Liu ◽  
X. P. Feng ◽  
Y. He ◽  
Y. D. Bao ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Hyperspectral Imaging ◽  
Imaging System ◽  
Vegetation Indices ◽  
Stem Rot ◽  
Sclerotinia Stem Rot ◽  
Linear Discriminant ◽  
Discriminant Models ◽  
Selection Of

A ground-based hyperspectral imaging system covering the spectral range of 384–1034 nm was used for Sclerotinia Stem Rot (SSR) detection. Two sample sets of oilseed leaves were collected. Four vegetation indices were extracted and evaluated by analysis of variance (ANOVA) combined with linear discriminant analysis (LDA) for the two sample sets. Discriminant models were built using the 4 vegetation indices. The discriminant results of the two sample sets were good with classification accuracies of the calibration set and the prediction set over 85%. The overall results indicated that vegetation indices calculated from ground-based hyperspectral imaging could be used as reliable and accurate indices for SSR detection.

Artificial Neural Networks and Linear Discriminant Analysis:  A Valuable Combination in the Selection of New Antibacterial Compounds

2004 ◽  
Vol 44 (3) ◽  
pp. 1031-1041 ◽  
Author(s):  
Miguel Murcia-Soler ◽  
Facundo Pérez-Giménez ◽  
Francisco J. García-March ◽  
Ma Teresa Salabert-Salvador ◽  
Wladimiro Díaz-Villanueva ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Antibacterial Compounds ◽  
Linear Discriminant ◽  
Artificial Neural ◽  
Selection Of

Artificial Neural Networks and Linear Discriminant Analysis: A Valuable Combination in the Selection of New Antibacterial Compounds.

ChemInform ◽  
2004 ◽  
Vol 35 (30) ◽  
Author(s):  
Miguel Murcia-Soler ◽  
Facundo Perez-Gimenez ◽  
Francisco J. Garcia-March ◽  
Ma. Teresa Salabert-Salvador ◽  
Wladimiro Diaz-Villanueva ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Antibacterial Compounds ◽  
Linear Discriminant ◽  
Artificial Neural ◽  
Selection Of

Classification of West German Business Cycles 1955-1994 / Klassifikation westdeutscher Konjunkturzyklen 1955 - 1994

1999 ◽  
Vol 219 (5-6) ◽  
Author(s):  
Ullrich Heilemann ◽  
Heinz Josef Münch
Keyword(s):  
Discriminant Analysis ◽  
Business Cycles ◽  
Linear Discriminant Analysis ◽  
Turning Point ◽  
Explanatory Power ◽  
Linear Discriminant ◽  
Fourth Cycle ◽  
Selection Of

SummaryThis paper applies multivariate linear discriminant analysis to classify West German business cycles from 1955 to 1994 into a four phase scheme (upswing, downswing, and upper/lower turning point phases). It describes the scheme as well as the selection of the classifying variables, and presents classification results for various sample periods confirming the four phase scheme. Special attention is given to changes of the explanatory power of the variables and its implication for changes of cycle patterns. While for the 1963/94 period the results display all in all much stability, the 1955/62 period does not fit into this pattern, indicating a change of regime which might even reach into the fourth cycle (1963-1967).

scholarly journals Prediction of Depression in Cancer Patients With Different Classification Criteria, Linear Discriminant Analysis versus Logistic Regression

2015 ◽  
Vol 8 (7) ◽  
pp. 41 ◽  
Author(s):  
Zahra Shayan ◽  
Naser Mohammad Gholi Mezerji ◽  
Leila Shayan ◽  
Parisa Naseri
Keyword(s):  
Logistic Regression ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Cancer Patients ◽  
Classification Error ◽  
Model Assessment ◽  
Continuous Variables ◽  
Optimal Model ◽  
Linear Discriminant ◽  
Selection Of

<p><strong>BACKGROUND: </strong>Logistic regression (LR) and linear discriminant analysis (LDA) are two popular<strong> </strong>statistical models for prediction of group membership. Although they are very similar, the LDA makes more assumptions about the data. When categorical and continuous variables used simultaneously, the optimal choice between the two models is questionable. In most studies, classification error (CE) is used to discriminate between subjects in several groups, but this index is not suitable to predict the accuracy of the outcome. The present study compared LR and LDA models using classification indices.</p><p><strong>METHODS:</strong> This cross-sectional study selected 243 cancer patients. Sample sets of different sizes (n = 50, 100, 150, 200, 220) were randomly selected and the CE, B, and Q classification indices were calculated by the LR and LDA models.</p><p><strong>RESULTS:</strong> CE revealed the a lack of superiority for one model over the other, but the results showed that LR performed better than LDA for the B and Q indices in all situations. No significant effect for sample size on CE was noted for selection of an optimal model. Assessment of the accuracy of prediction of real data indicated that the B and Q indices are appropriate for selection of an optimal model.</p><p><strong>CONCLUSION:</strong> The results of this study showed that LR performs better in some cases and LDA in others when based on CE. The CE index is not appropriate for classification, although the B and Q indices performed better and offered more efficient criteria for comparison and discrimination between groups.</p>

scholarly journals A Method for Detecting Incipient Faults in Satellites Based on Dynamic Linear Discriminant Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Ge Zhang ◽  
Qiong Yang ◽  
Guotong Li ◽  
Jiaxing Leng
Keyword(s):  
Discriminant Analysis ◽  
Fault Detection ◽  
Linear Discriminant Analysis ◽  
Historical Data ◽  
Detection Methods ◽  
Linear Discriminant ◽  
Projection Vector ◽  
Optimal Projection ◽  
Incipient Faults ◽  
F Distribution

Timely detection and treatment of possible incipient faults in satellites will effectively reduce the damage and harm they could cause. Although much work has been done concerning fault detection problems, the related questions about satellite incipient faults are little addressed. In this paper, a new satellite incipient fault detection method was proposed by combining the ideas of deviation in unsupervised fault detection methods and classification in supervised fault detection methods. First, the proposed method uses dynamic linear discriminant analysis (LDA) to find an optimal projection vector that separates the in-orbit data from the normal historical data as much as possible. Second, under the assumption that the parameters obey a multidimensional Gaussian distribution, it applies the normal historical data and the optimal projection vector to build a normal model. Finally, it employs the noncentral F-distribution to test whether a fault has occurred. The proposed method was validated using a numerical simulation case and a real satellite fault case. The results show that the method proposed in this paper is more effective at detecting incipient faults than traditional methods.

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