Research On Machine Fault Pattern Classification Based On Support Vector Machine

Machine fault diagnosis is a problem of pattern classification in nature. Because of the excellent performance on classification, support vector machine (SVM) is more and more widely used to solve classified problems in practical world. Based on SVM theory, this dissertation develops a research on machine fault pattern classification for engineering project applications.The thesis first introduces the history, the significance and the current research status of machine fault diagnosis. After analyzing the virtue and shortcoming of current fault diagnosis theories, the paper summarizes the application background of machine fault diagnosis. In the field of auto detection, driving axle is a key assembly component. The development of driving axle test system is described. The SVM theory and its research status in nation and oversea is analyzed. The availability of SVM application on machine fault diagnosis is discussed. Through the above analysis, a structure of fault diagnosis based on SVM theory is founded. In the end of this chapter, the main research content and method path of this thesis is proposed.Chapter two introduces the theory and arithmetic of SVM. After reviewing the basic theory-statistical learning theory (SLT) and machine learning (ML), the thesis discusses the SLT's structure and the question which SLT is faced. Based on analyzing the principle and arithmetic of SVM, three classification methods are introduced to present theory matting for latter chapters.Chapter three does a research on two class classifier problem of fault diagnosis for driving axle. Feature extraction and diagnosis decision-making are two parts of fault diagnosis. Because of the non-linearity charactering of axle faulty in fatigue experimentation, the paper firstly proposes kernel primary component analysis (KPCA) method to extract the features from normal and fault samples in the course of fatigue test. Then the kernel primary components are used as the input of SVM classifier. For kernel function is used both in KPCA and SVM, the paper proposes the concept of kernel SVM and uses genetic arithmetico (GA) to optimize the compromise parameters of kernel function and SVM. Under the disassembly situation...