Fault Diagnosis Model for the Regenerative Heating System of a Steam Turbine Unit Based on a Rough Set Theory
Zhao Xue-zeng · Journal of Engineering for Thermal Energy and Power · 2003
After an analysis of the insufficiency of current fault diagnostic methods used for the regenerative heating system of a steam turbine to resolve the problem of redundant fault symptoms the authors have proposed a new fault diagnosis model based on a rough set theory. With the typical fault modes of a regenerating heating system being taken into account a fault diagnostic decision table was established through a discretization of continuous fault symptom attributes. A reduction of the fault symptom attributes was realized by making use of a genetic algorithm. An optimal selection stratagem of minimal reduction is proposed based on domain knowledge. Then, a decision rules base for fault diagnosis was set up through the basic principle of decreasing the given decision rules. When the proposed model is employed for fault diagnosis the discretized fault symptom attributes to be diagnosed are first matched with the diagnostic decision rules in the rules base. The returned diagnostic decision rules will undergo a comprehensive evaluation with a diagnostic conclusion being reached. The simulation of typical faults by a power plant simulator was performed to verify the fault diagnosis model. Engineering practice shows that the proposed model is highly effective in reducing redundant fault symptoms and credited with a good fault-diagnosis effect as well as a fair fault-tolerant capability.