Study On Models And Methods Based On Integrated Diagnosis Network Of Steam Turbine Generator-Set's Vibration Faults

With the development of national economy, the ever-growing capacity of large steam generator-set demands higher usability, security, reliability and economical efficiency. Thus the research to the technology of the vibration fault diagnosis in the stream generator-set is of vial significance to its safe function, and can bring us substantial benefits. Based on the former study of the condition monitor and fault diagnosis on stream generator-set, this dissertation focuses on fuzzy set theory, artificial neural network technology, rough set theory and the genetic algorithms and theories on their integration mechanism. Attested by the cases of vibration fault of the present steam generator-sets, this dissertation draws some feasible conclusions, and brings forth some reliable and practical new methods, which have further enriched and greatly improved the theories on the vibration fault monitor and diagnosis of the large stream generator-set. The main contents of this paper are described as follows:1.The goals and meaning of this research are presented. It is a general survey to the feature of vibration faults of turbine generator-set, the present research and the research methods of on-line monitor and faults diagnosis, the development of Fuzzy Set Theory, Neural Network, Rough Set Theory and Genetic algorithms and their application in the generator-set vibration diagnosis.2.Based on the analysis of the electric, mechanic and thermo theories of steam turbine generator-sets' vibration faults, the application of the theory to the standardization of symptom data is put forward, which has provided a foundation for the research of intellectual diagnosis.3.On the analysis of the weaknesses of the BPNN theory and methods, genetic algorithms is adopted to optimize the initial weight, and through their organic integration, the optimal weight matrix of BPNN can be obtained, which can be successfully applied to diagnose the vibration fault of steam generator-set.4.On the analysis of rough set theory, the method to improve the traditional rough reduce weakness in avoiding the influence of interrupting attribute and the rough reduce algorithms based on genetic algorithm are brought forward. Suggested by simulation, this method has improved the exactness of technology requirement of diagnosis rules extraction and has been successfully used in vibration fault diagnosis in turbine generator-set. 5.In view of that vibration fault of large steam turbine generator-set has such features as complexity, non-linearity, multi influence factors and so on, the gradual discovery of diagnosis rules has been put forward and has been successfully used in actual fault diagnosis.6.The fuzzy neural network of steam turbine generator-set's vibration fault diagnosis based on rough set theory is brought forward, so as to compress the input dimension effectively, which has been proved to be successful by actual example. 7. Associated with achievements of this dissertation, the fuzzy set theory, neural network, genetic algorithm and rough set theory are integrated. The model of integrated diagnosis network of steam turbine generator-Set's Vibration Faults is constructed, and analysis on practical fault case is carried out, the results show that the model has a fairly high practical value.