Aircraft bleed valve fault classification using support vector machines and classification trees

2018 ◽  
Author(s):  
Henrique Mendes Castilho ◽  
Cairo Lucio Nascimento ◽  
Wlamir Olivares Loesch Vianna
Keyword(s):  
Support Vector Machines ◽  
Classification Trees ◽  
Fault Classification ◽  
Support Vector ◽  
Vector Machines

scholarly journals Bankruptcy Prediction of Engineering Companies in the EU Using Classification Methods

2018 ◽  
Vol 66 (5) ◽  
pp. 1347-1356 ◽  
Author(s):  
Michaela Staňková ◽  
David Hampel
Keyword(s):  
Logistic Regression ◽  
Support Vector Machines ◽  
Binary Classification ◽  
Classification Tree ◽  
Classification Trees ◽  
Bankruptcy Prediction ◽  
Support Vector ◽  
Type I ◽  
Vector Machines ◽  
The Eu

This article focuses on the problem of binary classification of 902 small- and medium‑sized engineering companies active in the EU, together with additional 51 companies which went bankrupt in 2014. For classification purposes, the basic statistical method of logistic regression has been selected, together with a representative of machine learning (support vector machines and classification trees method) to construct models for bankruptcy prediction. Different settings have been tested for each method. Furthermore, the models were estimated based on complete data and also using identified artificial factors. To evaluate the quality of prediction we observe not only the total accuracy with the type I and II errors but also the area under ROC curve criterion. The results clearly show that increasing distance to bankruptcy decreases the predictive ability of all models. The classification tree method leads us to rather simple models. The best classification results were achieved through logistic regression based on artificial factors. Moreover, this procedure provides good and stable results regardless of other settings. Artificial factors also seem to be a suitable variable for support vector machines models, but classification trees achieved better results using original data.

Coupling Pairwise Support Vector Machines for Fault Classification

2003 ◽  
Vol 36 (5) ◽  
pp. 657-662
Author(s):  
Sanna Pöyhönen ◽  
Antero Arkkio ◽  
Heikki Hyötyniemi
Keyword(s):  
Support Vector Machines ◽  
Fault Classification ◽  
Support Vector ◽  
Vector Machines

Gear Fault Diagnosis Based on Wavelet-Support Vector Machines

2010 ◽  
Vol 33 ◽  
pp. 450-453 ◽  
Author(s):  
Jie Zhao ◽  
Chun Hua Li
Keyword(s):  
Fault Diagnosis ◽  
Support Vector Machines ◽  
Classification Model ◽  
Fault Classification ◽  
Small Samples ◽  
Support Vector ◽  
Noise Interference ◽  
Vector Machines ◽  
Gear Fault ◽  
Vibration Noise

According to the characteristics of gear vibration noise large and fault diagnosis complex, the paper proposes the method of gear fault classification based on wavelet analysis - Support Vector Machines (SVM). This method effectively eliminates the noise interference of the gear signals. The classification model of gear diagnosis applicable to small samples is established and the result of simulation shows that the model can correctly realize gear fault.

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