Robust fault detection on boiler-turbine unit actuators using dynamic neural networks

Due to the important role of the boiler-turbine units in industries, it is important to diagnose different types of faults in boiler-turbine units. Actuators as the main part of the system can be affected by different types of faults. In this paper fault detection of boiler-turbine actuators is studied. In order to detect the fault, a dynamic neural network with an internal feedback is applied. After generating the residuals, the decision making step has to be followed. In order to design a proper threshold which is sensitive to different types of faults and insensitive to noise, the robust threshold is designed using the model error modeling method. The results show the effectiveness of this approach for designing the threshold. As a practical case, the dynamic model of the boiler-turbine unit presented by Bell and Astrom is considered.

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