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

Yang Chuangyan, Li Peng, Lang Xun, Wu Jiande, Wang Hancheng

Eksploatacja i Niezawodność

2024

Vol. 26, no. 4

art. no. 190381

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 Gated Recurrent Unit (GRU) and Stack Sparse Autoencoder (SSAE) algorithm is proposed to solve this problem. Firstly, using NMF instead of Singular Value Decomposition(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 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. For the first time, dynamic latent variable analysis and deep learning representation algorithm are applied to icing failure identification of wind turbine blade, and effective results are obtained.