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Due to the complexity of the power plant production environment, it brings some difficulties to troubleshooting of turbine generator. Although the approach based on neural network has been widely used in fault diagnosis of equipment, the result of fault diagnosis, which is given by the single neural network, is often not ready to determine the fault type for turbine generator. In response to this...
By monitoring a 600MW steam turbine generator unit, its structure is analyzed to acquire the parameters which are used for keeping the safety of steam turbine generator unit and their value ranges. We constructed the functional diagram of condition monitoring and fault diagnosing system for steam turbine generator unit, which functions are described in detail as follows. By applying various kinds...
A new method of fault diagnosis for turbines using preserving nonlinear and quasi-Newton algorithm for the learning process of BP neural network was introduced in the paper, Which take advantages of preserving nonlinear and quasi-Newton algorithm with fast speed, significant superiority for high-dimensional problems and more accurate mathematical models comparing with traditional BP neural network...
According to the insufficiencies in condenser fault diagnosis based on single neural network, a new method of condenser fault diagnosis based on neural network and information fusion has been proposed this paper. By means of grouping fault symptoms, the different neural networks have been adopted to diagnose faults. And the results are composed of the preliminary fault diagnosis synthetic matrix....
In order to enhance the accuracy rate of the fault diagnosis of condenser, a fault diagnosis method based on consensus information fusion is presented. The decision-making distance is calculated and the closest supporting group is found. By integrating the results of the closest supporting group, the best decision-making value of fault diagnosis is obtained. This method is applied in the simulation...
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