Fault Diagnosis Of Turbine Steam Leakage Based On Soft-Sensor

With the development of industrial technology, the requirements for steam turbine operating condition monitoring requirements are increasing, Although the level of monitoring has been made substantial progress, there are still many problems to be resolved. Steam leakage occurred in the steam turbine stages not only influences the safe of steam turbine, it would also affect the steam turbine heat economy. Since the steam turbine working environment is complex, there are not good tools for steam leakage monitoring. This lets the staff unable to catch the steam leakage trend. If serious steam leakage problems happened, it can be addressed only on the overhaul. So finding the steam leakage changes in a timely manner is essential.To counter the problem, it is proposed in this paper a new measure of steam leakage with soft-sensor. The main work contains the following aspects:1. Establishing the steam turbine leakage simulation modelThe mathematical model is one effective analysis method, for complex systems. We build a simplified steam turbine model, explain each part of work mechanism and the mathematical model in detail. Then we analyze the steam leakage phenomenon in detail. The leakage of steam is characterized with steam leak factor. The coefficient also depicts a steam leakage level within the leaking area change. We study the steam leakage phenomenon for a specific simulation. The simulation provides a large number of data for the steam leakage prediction.2. Steam leakage prediction model based on the least-squaresFrom engineering perspective, there is some noise in the data acquired from sensors. The noise will affect the accuracy of model estimates. Before modeling we should discuss the noise in data. Chapter III analyzes the influence of some typical classes of noise. Then we use the least-squares to establish the steam leakage estimation model. The experiments indicate that the model can restrain the noise.3. Steam leakage estimation model based on Artificial Neural NetworkIn recent years as a view hot topic of, artificial neural networks provide another kind of view to the steam leakage estimation. Chapter IV utilizes neural networks modeling methods. To construct predicting model for leakage, we introduce a priori knowledge of the target to improve model accuracy and generalization, the test shows that the model has good generalization ability. Finally we compare the neural networks model with the least-squares model, we discuss their merits and disadvantages.