Steam turbine fault early warning algorithm based on association rules and multivariate state estimation

Abstract

In order to accurately achieve steam turbine fault warning, a steam turbine fault warning algorithm based on association rules and multivariate state estimation is proposed. Mining different types of turbine fault features from historical data using association rules to construct original decision tables; Conduct attribute reduction on the results of association rule mining and extract the optimal set of fault feature reduction; Using the optimal attribute set combination reduction decision table to classify different types of turbine fault information. Based on historical data of steam turbine faults and rea1-time monitoring data , a dynamic memory matrix is constructed, and the fault state of the steam turbine is calculated using multivariate state estimation method. The fault feature vector is calculated using nonlinear Euclidean distance to estimate the deviation distance from the observed data. Use similarity function to estimate the state of the steam turbine, set fault alarm threshold, and achieve steam turbine fault warning. The test results show that the proposed algorithm can accurately classify the types of steam turbine faults, and the estimated values are basically consistent with the actual values, which can accurately achieve steam turbine fault warning.