Discriminative dictionary learning based sparse representation classification for intelligent fault identification of planet bearings in wind turbine

Planet bearing fault identification is an attractive but challenging task in wind turbine condition monitoring and fault diagnosis. Traditional fault characteristic frequency identification based diagnostic strategies are not sufficient for reliable planet bearing fault detection, due to complex physical configurations and modulation characteristics in planetary drivetrains. In this paper, we propose a discriminative dictionary learning based sparse representation classification (DDL-SRC) framework for intelligent planet bearing fault identification. Our framework could learn a reconstructive and discriminative dictionary for signal sparse representation and an optimal linear classifier for classification tasks simultaneously, which bridges the gap between dictionary learning and classifier training in traditional SRC methods. Specifically, the optimization objective introduces a novel regularization term called ‘discriminative sparse codes error’ and incorporates it with the reconstruction error and classification error. Thus, the dictionary learned by our framework possesses not only the reconstructive power for sparse representation but also the discriminative power for classifier training. The optimization formulation is efficiently solved using K-SVD and orthogonal matching pursuit algorithms. The experiment validations have been conducted for demonstrating the effectiveness and superiority of the proposed DDL-SRC framework over the state-of-the-art dictionary learning based SRC and deep convolutional neural network methods for intelligent planet bearing fault identification.