Fault Forecast and Diagnosis of Steam Turbine Based on Fuzzy Rough Set Theory
Qinglin Guo, Kehe Wu, Wei Li · 2007
A novel approach for fault forecast and diagnosis of steam turbine based on rough set data mining theory is brought forward, aimed at overcoming shortages of some current knowledge attaining methods. The historical fault data of steam turbine is processed with fuzzy and scatter method. The processed data is used to structure the fault diagnosis decision-making table that is treated as "knowledge database". This paper introduced rough sets data mining method to take potential diagnosis rule from the fault diagnosis decision-making table of steam turbine. These rules can offer effective fault diagnosis service for steam turbine. The algorithm for classified rule learning and reducing is brought forward, and an experimental system for fault forecast and diagnosis of steam turbine based on rough set data mining theory is implemented. Their diagnosis precision is above 88%. And experiments do prove that it is feasible to use the method to develop a system for fault forecast and diagnosis of steam turbine, which is valuable for further study in more depth.