Kernel independent component analysis and dynamic selective neural network ensemble for fault diagnosis of steam turbine

摘要: A new method for fault diagnosis of steam turbine based on kernel independent component analysis (KICA) and dynamic selective neural network ensemble is proposed Firstly, the data analyzed using KICA to extract main features from high dimensional patterns Not only diagnosing efficiency improved but also accuracy ensured Then, generalization errors different networks each validating sample are calculated information collected into a performance matrix, according which K-nearest neighbor algorithm used predict testing Lastly, individual whose in threshold λ will be dynamically selected predictions combined through majority voting The practical applications show that approach gives promising results even with smaller learning samples, it has higher stability.