Study Of Fault Diagnosis Based On Signed Directed Graph And Support Vector Machine

Signed Directed Graph is a fault diagnosis method developed on the basis of graph theory, and not only can efficiently express the interrelations among variables of complex systems and has strong completeness, flexible reasoning ways and effective reasoning algorithm, but also shows failure propagation paths and faults detailed explanations. However, SDG is an qualitative analysis method, many useful quantitative information in measured signals are ignored or cannot be considered, which leads to low fault resolution. Support Vector Machine (S VM) is a new kind of machine learning method developed on the basis of statistical learning theory, which uses structural risk minimization thought. SVM takes the minimum of experience risk and confidence interval into account, and can gain the best generalization ability, and specializes in small sample. Then, SVM ingeniously uses "Kernel function", which can map low-dimensional nonlinear space to higher dimensional linear character space, in the process of which don't increase the complexity of solution of optimal classification, and solve problem of "dimension disaster" brought by calculation in higher-dimensional space. With profound mathematics base and strong generalization ability, SVM is considered to be one of the most influential achievements in pattern recognition and machine learning domain in ten years.The qualitative SDG and quantitative SVM are combined in this paper. Mainly related variables in compatible pathways can be got by SDG's completeness and reasoning mechanisms; main variables are trained to acquire optimal hyperplanes.This paper mainly includes two parts:first, support vector pre-selected method based on Central Samples Discarded Method is proposed and is applied in Pattern Recognition; second, fault diagnosis algorithm based on SDG and SVM is proposed and applied in engineering fault diagnosis, which is this paper's core.The main research and innovative achievements in this paper can be classed as follows:(1) On the basis of the thorough research in theoretical basis of support vector machine and the basic principle, support vector pre-selected was proposed based on the method of "Central Samples Discarded Method, CSDM". Support vector is the key elements deciding the optimal hyperplanes location, and removing the non-support vector and restarting training the sample can get the same optimal hyperplanes. Based on thought above, the method of "CSDM", which removes the samples near the center and keeps the boundary samples to have support vector pre-selected, is put forward, thus the purpose of improving the training speed is achieved. Compared with pre-existing support vector pre-selected method, the feasibility is demonstrated.(2) Put forward the concept of Initial Consistent Path. Initial Consistent Path refers to consistent path at the beginning of the faults. When a fault occurs, the response of the system state variables has three stages:the initial response, middle response and eventual response. One of the main performance indexes of fault diagnosis is real-time. Therefore, the gain of consistent path of the initial response phase for fault diagnosis is very important, which can effectively solve the problem that different compatible pathways in different fault period can lead to low resolution. Initial Consistent Path is an extension to SDG.(3) Put forward to fault diagnosis method of the combination of SDG and SVM. Proposed compatible pathways can look for fluctuating variables at the begining of the faults occurance. SVM training based on these variables can achieve the purpose of reducing the dimension, improve training and diagnosis velocity; SVM excellent classification can improve fault diagnosis accuracy. Fault diagnosis for deaerator of coal-fired plants demonstrated the feasibility of this method. (4) Fault diagnosis method based on SDG and SVM is applied into Tennessee-Eastman Process (TEP) simulation fault diagnosis system. By analyzing the experimental results of the TEP simulation system, this paper discusses the applicable scope of the fault diagnosis method.Innovative achievements in this paper can be classed as follows:(1) Support vector pre-selected was proposed based on the method of Central Samples Discarded Method.(2) Put forward to the concept of Initial Consistent Path by failure propagation characteristics.(3) Put forward to fault diagnosis algorithm based on SDG and SVM.(4) On account of the characteristics of special faults in multi-fault diagnosis, mix multi-class classification algorithm constituted with the combination of classification algorithms based on binary tree and DDAG are proposed and applied in fault diagnosis for deaerator of power plants.(5) In DDAG-classification algorithm, the concept that different variables for classifier training in different two classification problem is put forward base on Initial Consistent Path, which improves training speed of algorithms.