Robust Fault Detection on Boiler-turbine Unit Actuators Using Dynamic Neural Networks

Due to the important role of the boiler-turbine units in industries and electricity generation, it is important to diagnose different types of faults in different parts of boiler-turbine system. Different parts of a boiler-turbine system like the sensor or actuator or plant can be affected by various types of faults. In this paper, the effects of the occurrence of faults on the actuators are investigated and analyzed and fault detection of boiler-turbine actuators is studied. For fault detection purpose, a dynamic neural network with an internal feedback is applied to generate the residual. After generating the residuals, the decision making step, as the most crucial part of the fault detection process, has to be followed. For designing 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 robust threshold is designed using a dynamic neural network with an internal feedback. The results for multiple types of faults and various outputs show the effectiveness of this approach for designing the threshold. As a practical case of study the dynamic model of the boiler-turbine unit, which was represented by Bell and Astrom in their paper, is considered.