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Fault Diagnosis of Induction Motor using Decision Tree with An Optimal Feature Selection
Ngoc-Tu Nguyen,Jeong-Min Kwon,Hong-Hee Lee 전력전자학회 2007 ICPE(ISPE)논문집 Vol.- No.-
Time vibration signals are measured to extract a feature set for fault diagnostics of induction motor. Feature selection by decision tree and genetic algorithm (GA) is presented in this paper to remove irrelevant information in the feature set. New data with the selected features is used to train a decision tree, which is an expert system for classification. Testing results show that systems with selected features can reliably diagnose different conditions of induction motor, which has better performance compared to original one without feature selection.
Nguyen, Ngoc-Tu,Kim, Yang-Hoon,Bang, Seung Hyuck,Hong, Ji Hye,Kwon, Soon Dong,Min, Jiho The Korean Society of Environmental Toxicology 2014 환경독성보건학회지 Vol.29 No.-
Objectives Lysosome is the cell-organelle which is commonly used as biomonitoring tool in environmental pollution. In this study, the lysosomal proteomic of the yeast Saccharomyces cerevisiae was analyzed for utilization in the detection of toxic substances in mine water samples. Methods This work informs the expression of lysosomal proteomic in yeast in response with toxic chemicals, such as sodium meta-arsenite and tetracycline, for screening specific biomarkers. After that, a recombinant yeast contained this biomarker were constructed for toxic detection in pure toxic chemicals and mine water samples. Results Each chemical had an optimal dose at which the fluorescent protein intensity reached the peak. In the case of water samples, the yeast showed the response with sample 1, 3, 4, and 5; whereas there is no response with sample 2, 6, and 7. Conclusions The recombinant yeast showed a high ability of toxic detection in response with several chemicals such as heavy metals and pharmaceuticals. In the case of mine water samples, the response varied depending on the sample content.
A Study on Machine Fault Diagnosis using Decision Tree
Nguyen, Ngoc-Tu,Kwon, Jeong-Min,Lee, Hong-Hee The Korean Institute of Electrical Engineers 2007 Journal of Electrical Engineering & Technology Vol.2 No.4
The paper describes a way to diagnose machine condition based on the expert system. In this paper, an expert system-decision tree is built and experimented to diagnose and to detect machine defects. The main objective of this study is to provide a simple way to monitor machine status by synthesizing the knowledge and experiences on the diagnostic case histories of the rotating machinery. A traditional decision tree has been constructed using vibration-based inputs. Some case studies are provided to illustrate the application and advantages of the decision tree system for machine fault diagnosis.
Decision Tree with Optimal Feature Selection for Bearing Fault Detection
Nguyen, Ngoc-Tu,Lee, Hong-Hee The Korean Institute of Power Electronics 2008 JOURNAL OF POWER ELECTRONICS Vol.8 No.1
In this paper, the features extracted from vibration time signals are used to detect the bearing fault condition. The decision tree is applied to diagnose the bearing status, which has the benefits of being an expert system that is based on knowledge history and is simple to understand. This paper also suggests a genetic algorithm (GA) as a method to reduce the number of features. In order to show the potentials of this method in both aspects of accuracy and simplicity, the reduced-feature decision tree is compared with the non reduced-feature decision tree and the PCA-based decision tree.
Decision Tree with Optimal Feature Selection for Bearing Fault Detection
Ngoc-Tu Nguyen,Hong-Hee Lee 전력전자학회 2008 JOURNAL OF POWER ELECTRONICS Vol.8 No.1
In this paper, the features extracted from vibration time signals are used to detect the bearing fault condition. The decision tree is applied to diagnose the bearing status, which has the benefits of being an expert system that is based on knowledge history and is simple to understand. This paper also suggests a genetic algorithm (GA) as a method to reduce the number of features. In order to show the potentials of this method in both aspects of accuracy and simplicity, the reduced-feature decision tree is compared with the non reduced-feature decision tree and the PCA-based decision tree.