Study on Monitoring Data Mining of Steam Turbine Based on Interactive Association Rules

Real-time monitoring data mining has been a necessary means of improving operational efficiency, economic safety and fault detection of power plant. Based on the data mining arithmetic of interactive association rules and taken full advantage of the association characteristics of real-time test-spot data during the power steam turbine run, the principle of mining quantificational association rule in parameters is put forward among the real-time monitor data of steam turbine. Through analyzing the practical run results of a certain steam turbine with the data mining method based on the interactive rule, it shows that it can supervise stream turbine run and condition monitoring, and afford model reference and decision-making supporting for the fault diagnose and condition-based maintenance.