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Research On Fault Diagnosis For Hydraulic System Based On Support Vector Machine

Posted on:2006-03-10Degree:MasterType:Thesis
Country:ChinaCandidate:Q YangFull Text:PDF
GTID:2168360155964975Subject:Control theory and control engineering
Abstract/Summary:PDF Full Text Request
SVM (Support Vector Machines), which is based on Vladimir N. Vapnik's Statistical Learning Theory, now is the most advanced machine learning algorithm in the field of pattern recognition, and its characters have already showed more superiority than other methods. It can solve small sample learning problems better by using Structural Risk Minimization than Empirical Risk Minimization. Moreover, by using the kernel function idea, this theory can change the problem in non-linearity space to that in the linearity space in order to reduce the algorithm complexity. SVM have become the hotspot of machine learning because of their excellent learning performance. They also have successful applications in many fields, such as: handwriting digit recognition, face detection and so on. But as a new technique, SVM also have many shortcomings that need to be tracked and bettered, including: the adaptive kernel and parameter selection, the shortcomings of training methods and incremental learning, etc. Because of these problems, the applications of SVM are limited in many fields.In this thesis, the author discusses the applications of SVM in the hydraulic fault diagnosis while studying the arithmetic of SVM. Regard common hydraulic equipment crane as research object, the author carries on simulation research and experiment research of fault diagnosis by using SVM theory, including: design of the mathematics model, simulation model (by SIMULINK software), extraction of the Feature parameter and data collection of the experimental system.
Keywords/Search Tags:Statistical Learning Theory, Support Vector Machines, Kernel Function, Feature Extraction, Fault Diagnosis
PDF Full Text Request
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