A novel NMF-DiCCA deep learning method and its application in wind turbine blade icing failure identification

Wind turbine blade icing data has the characteristics of multi-source and multi variable. It is difficult to characterize and identify the icing failure with multi-scale features. In this paper, a novel Non-negative Matrix Factorization-Dynamic-inner Canonical Correlation Analysis (NMF-DiCCA )based on GRU and SSAE algorithm is proposed to solve this problem. Firstly, using NMF instead of SVD decomposition method in DiCCA algorithm, the NMF-DiCCA is applied to obtain the dynamic latent variable of time serie. Secondly, the latent structure features S of dynamic latent variable is captured by SSAE. Thirdly, the temporal correlation hidden feature H of dynamic latent variable is extracted by Gated Recurrent Unit (GRU). Finally, the attention weight distribution between latent structure S and temporal correlation hidden feature H is integrated using the attention mechanism, and the fusion feature is reconstructed using the improved SSAE(ISSAE) based on GRU and SSAE.