A Decision Tree Approach for Steam Turbine-Generator Fault Diagnosis

Redundancy and inconsistency are universal features of the turbine vibration fault diagnosis. If we can provide a solution to the problem, it should be very meaningful that the fault diagnosis data included redundant and inconsistent information could be used to decision-making rules of fault diagnosis. A novel data mining approach for fault diagnosis of turbine generator unit is proposed based on a decision tree in this paper. In terms of history samples library of turbine generator faults, the method applies entropy-based information gain as heuristic information to select test attributes, and uses ID3 algorithm to generate the decision tree and distilling classification rules are handled. The research shows the method not only possesses rapid induction learning ability and classification speed, but also can effectively compress data and save memory, and is an effect turbine generator fault diagnosis method. In the end, a practical application indicates the validities of the method.