Abstract:

Based on  K means clustering algorithms and Gaussian mixture model, and a power plant’s 320MW unit historical operation data, Gaussian mixture model of steam turbine shaft was built. By analyzing the similarity of realtime operation and expectation value from model, combined with steam turbine shaft failure database, ultimately realized steam turbine shaft failure diagnostic.

Key words:

steam turbine shaft, Gaussian mixture model, K means clustering algorithms, failure diagnostic