Study On Fault Diagnosis Based-on SVM And Remote Condition Monitoring For Turbine Generator Unit Vibration

With the rapid development of power industry, it asks more on the condition monitoring and fault diagnosis of turbo-generator. The paper made an intensive and valuable study on several key technologies associated with the development of fault diagnostics, which include attribute extraction of vibration fault, multi-symptom fault diagnosis, vibration condition monitoring, vibration fault trend forecasting and remote online monitoring and diagnosis.Characteristics of vibration fault and fault symptoms of turbine generators are studied. A few new methods are used for attribute extraction of vibration fault symptom. A Mann-Kendall test is used to extract trend symptom of vibration parameters; an EMD method and a correlation coefficient one are used to extract correlation symptom; an invariant moment method is used to extract graphical symptom of shaft center orbit.A new machine learning method-Support Vector Machines, has been used to detect vibration abnormality, predict vibration and fault trend, and to diagnose vibration fault.Based on analysis of One-Class SVM theory, a new vibration abnormality detection method has been put forward for steam turbine generator units. This One-Class SVM abnormality detection method, require only samples gathered when turbine generators are in good order, which finds a solution to lack of abnormal training samples. Comparison tests with Fuzzy ART1 and ARTMAP neural networks the method this thesis suggested have a good performance.A multi-class classifier has been bring forward and been used for vibration fault diagnosis, which shorten time when training classifier with fault samples, and get rid of the rejection classifying, which will be found in some multi-svm classifiers, such as the one-against-one SVM one. Another fault diagnosis model using One-Class SVM has also been made, which extends capability of diagnosis model by add new One-Class SVM classifier dynamically.The theory of support vector regression and the application of LS-SVM in time series prediction have been studied. An emulational test has been made which proves LS-SVM can have a good performance with a time series polluted by noise and get a better result than RBF neural networks. This method was used to predict vibration and fault trend for turbine generators, which can have a more accurate result. At last of the thesis, by using Java programming language, an Internet-based remote condition monitoring and fault diagnosis system was been developed, which takes full advantage of Java Applet, Socket, multithreads and Web database technology, and make true remote monitoring and fault diagnosis.