Dissertation > Excellent graduate degree dissertation topics show

Fault Diagnosis Method Based on Support Vector Machine

Author: ChenHuanHuan
Tutor: ShenYi
School: Harbin Institute of Technology
Course: Control Science and Engineering
Keywords: Support Vector Machine Fault diagnosis Decision Tree Support Vector Machine Directed Acylic Graph Rough Set
CLC: TP18
Type: Master's thesis
Year: 2008
Downloads: 345
Quote: 0
Read: Download Dissertation

Abstract


Intelligent diagnosis technology stands for the development direction of diagnosis technology, and it is associated with artificial intelligent technology, and provides the possibilities of intelligentization for fault diagnosis. Support Vector Machine(SVM) based on Statistical Learning Theory(SLT), is a powerful method for machine learning. SVM realizes Structural Risk Minimization Principle, which can effectively solves the problems of nonlinearity, limited samples, and high dimension, usually provides good generalization ability, and has been successfully applied to various areas including fault diagnosis.In the thesis, some issues in the field of SVM for intelligent fault diagnosis are studied. Firstly, a brief introduction of fault diagnosis technology and SVM is given, then the research of SVM in theory and application is presented. Secondly, the foundational issues in machine learning and SLT are summarized, and the basic principles and performance of SVM are discussed, besides, the methods for multi-class based on SVM are analyzed. Thirdly, we study the multi-class classification method based on Decision Tree SVM(DTSVM). In respect that the performance’s relation with the structure of DTSVM, Genetic Algorithm is used to solve combinatorial optimization problems during the formation of Decision Tree. Fourthly, we analyze the multi-class SVM based on Directed Acylic Graph(DAG). This method is equivalent to operating on a list and depends on node sequence in DAG, so separability measure is introduced to organize the list and sequence to design DAG. Finally, the classification method of SVM based on Rough Set(RS) is discussed. Using the concept of distribution weight ,we redefine dependency degree and attribute importance of RS, and improve the algorithm of attribute reduction.The study in this thesis is very important to intelligent diagnosis technology, and also useful for SVM.

Related Dissertations

  1. Design and Development of Application Programme for Fault Analyzer Based on Wince Platform,TP311.52
  2. Research on the 6-Dof Fault Tolerant Control of the Vibration Isolation Platform with Eight Actuators,TB535.1
  3. Research on Automatic Detection Algorithm for Substructure Distress of Highway Pavement Based on SVM,U418.6
  4. Research on Autamatic Music Structrue Analysis,TN912.3
  5. Research on Transductive Support Vector Machine and Its Application in Image Retrieval,TP391.41
  6. Fault Diagnosis Research on Three-Tank System,TP277
  7. Process Support Vector Machine and Its Application to Satellite Thermal Equilibrium Temperature Prediction,TP183
  8. Research on the Key Technology of Waterborne Transport Security System,U698
  9. Research for Infrared Image Target Identification and Tracking Technology,TP391.41
  10. Research on Clustering Algorithm Based on Genetic Algorithm and Rough Set Theory,TP18
  11. Based on Rough Set of Urban Areas When Traffic Green Control System Research,TP18
  12. Study on the Road Condition Monitoring Based on Vehicular 3D Acceleration Sensor,TP274
  13. Research of Diagnosing Cucumber Diseases Based on Hyperspectral Imaging,S436.421
  14. Incremental rough set attribute reduction,TP18
  15. Calculation of Knowledge Granulation and Study of Its Application in Attribute Reduction,TP18
  16. Research of License Plate Recognition Based on Rough Sets and Fuzzy SVM,TP391.41
  17. The Research on Intrusion Detection System Based on Machine Learning,TP393.08
  18. Research on Improved K Neighbor Support Vector Machine Algorithm Faced Text Classification,TP391.1
  19. Research on Feature Extraction, Selection and Classification Algorithms for Pulmonary CAD,TP391.41
  20. Research on Subimage Selection and Mathching Method for Synthetic Aperture Radar(SAR) Target Recognition,TN957.52

CLC: > Industrial Technology > Automation technology,computer technology > Automated basic theory > Artificial intelligence theory
© 2012 www.DissertationTopic.Net  Mobile