Vibration Fault Diagnosis of Rotating Machinery in Power Plants

This paper presents a novel data mining approach for fault diagnosis of turbine-generator units. The proposed rough set theory based approach generates the diagnosis rules from inconsistent and redundant information using genetic algorithm and process of rule generalization. In this paper, a fault diagnosis decision table is obtained from discretization of continuous symptom attributes in the data set. Then, the proposed genetic algorithm is used to achieve the minimal reduct from the discretized symptom attributes. In addition, a set of maximal generalized decision rules is obtained from the proposed rule generalization process.