| With the increasing capacity of steam turbine generator-set and the complexity of its structure,the relations among components are getting closer than ever. Although the causes resulting in a machine set's vibration become more various and complex,the vibrant state is still a very important indication to judge the working conditions of machine set. At the same time,steam turbine generator-set is a system,which often has multiple faults synchronously,and the problem for the multiple faults has not been solved appropriately. Therefore,the research on vibration fault diagnosis not only has great importance and benefit for a machine set to work safely and stably,but also is a frontier topic for electrical engineering and other cross fields. Based on predecessors' studies on steam turbine generator-set's state monitoring and fault diagnose,this paper focuses on the models and methods used to judge the multiple faults. These models and models,which are based on fuzzy sets theory,probability causal model,artificial neural network technology and genetic algorithms,are tested by some existing examples and some useful conclusions are drawn. All above will put the theory of multiple fault diagnosis of a large turbine generator set a further step.It's well known that the vibration fault diagnosis technology on steam turbine generator-set is a very complicated system project,which involves many fields and has a close relationship with practice. So it always draws much attentions from engineering field.The main contents and the creationary outcomes of this paper are described as follows:(1) The limitation of the fuzzy general judge is found by analyzing its application in steam turbine generator-set's fault diagnosis and a multi-layer fuzzy diagnosis model which can overcome these limitations is put forward.(2) A new method,which can be used to diagnose steam turbine generator-set's multiple faults,is brought out. This method,which is based on fuzzy clustering analyze theory,puts standard fault samples and the checked samples together as classified samples and draw a conclusion by using transfer closure based on fuzzy equivalence matrix.(3) The probability causal model and the connective model based on the probability causal for the fault diagnosis of steam turbine generator-set are put forward by the analysis of the relationships between the probability causal model and theparsimonious cover set theory.(4) Applying the relationships between faults and fealtures in probability causal model,the BP neural network is improved by adding the direct connections between the input nodes and the output nodes.(5) A two-layer neural network model for steam turbine generator set's multiple fault diagnosis is put forward. It can identify the multiple faults and has high modularization. Also it can find the new fault.(6) By the research of clustering capability of SOM,associated with fuzzy clustering analysis,we can realize the diagnosis of multiple faults of turbine generator-set.(7) By the analysis of the fuzzy-c partition and the genetic algorithm,a new method combing with them is put forward. This method can prevent the fuzzy-c partition from the local least point and can diagnose the steam turbine generator-set's multiple faults more effectively.(8) Considering that probability causal model has not a qualitative standard to select parsimonious rules and there could be a problem of "combination explosion" when there are so many faults in the model,a method which combines probability causal model and genetic algorithm is put forward.(9) A method,which combines the neural network and the genetic algorithms,is advanced. It uses the genetic algorithms to improve the weight and threshold of the neural network first. Then using the neural network,the diagnosis result can be gotten.The methods and models that this paper put forwards can diagnose the vibration multiple faults of steam turbine generator-set effectively,and this could be proved by examples. It enriches the theory and...
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