Fault detection and diagnosis for steam turbine based on kernel GDA

Abstract

A novel fault detection and diagnosis method based on kernel generalized discriminant analysis (kernel GDA, KGDA) is proposed in order to solve the problem of turbine fault detection and diagnosis. Through kernel GDA, the data is mapped from original space to the high-dimensional feature space. Then the statistic distance between normal data and test data is constructed to detect whether a fault is occurring. If a fault has occurred, similar analysis is used to identify type of the faults. The proposed method is scalable to different steam turbine and rotating machineries. Its effectiveness is evaluated by simulation results of vibration signal fault dataset.