An adaptive kernel dictionary learning method based on grey wolf optimizer for bearing intelligent fault diagnosis

In this study, an adaptive kernel dictionary learning method for intelligent fault diagnosis of bearings is proposed. Kernel KSVD (KKSVD) is an excellent dictionary learning method with the capacity to handle nonlinear signals. However, the choice of kernel parameters and sparse level is a key issue, since these parameters respectively determine the form of the high-dimensional kernel space and the capability of KKSVD to learn appropriate atomic information for representing the samples. As a result, it is difficult to achieve the maximum performance of KKSVD by pre-specifying the values of the parameters. To address this issue, an advanced meta-heuristic algorithm – that is, the grey wolf optimizer (GWO) is introduced into the KKSVD. Specifically, an objective function is first designed, in which the parameters to be optimized are involved in the learning process of KKSVD for the bearing train set and then applied to the testing of the bearing validation set to get the classification results. The classification accuracy is fed back to the GWO algorithm which will update the parameters iteratively and output the optimal parameters. Two case studies respectively corresponding to two common situations in bearing fault diagnosis – that is, strong noisy samples and unbalanced samples, are carried out. The analysis results demonstrate the effectiveness of the proposed method for adaptively obtaining the optimal parameters and improving the performance of KKSVD. Furthermore, the proposed method outperforms several state-of-art dictionary methods in terms of diagnosis accuracy and robustness.