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The nonlinear and nonstationary characteristics of vibration signal in mechanical equipment make fault identification difficult. To tackle this problem, this paper proposes a novel fault identification method based on improved variational mode decomposition (IVMD), multiscale permutation entropy (MPE), and adaptive GG clustering. Firstly, the original vibration signal is decomposed into a set of mode components adaptively by IVMD, and the mode components that are highly correlated with the original signal are selected to reconstruct the original signal. After that, the MPE values of the reconstructed signal are calculated as feature vectors which can differentiate machinery conditions. Finally, low-dimensional sensitive features obtained by principal component analysis (PCA) are fed into the adaptive GG clustering algorithm to perform fault identification. In this method, the residual energy ratio is used to find the optimal parameter K of the VMD and the PBMF function is incorporated into the GG to determine the number of clusters adaptively. Two bearing datasets are used to validate the performance of the proposed method. The results show that the proposed method can effectively identify different fault types. [ABSTRACT FROM AUTHOR]

Deep learning (DL) has been successfully used in fault diagnosis. Training deep neural networks, such as convolutional neural networks (CNNs), require plenty of labeled samples. However, in mechanical fault diagnosis, labeled data are costly and time-consuming to collect. A novel method based on a deep convolutional autoencoding network (DCAEN) and adaptive nonparametric weighted-feature extraction Gustafson–Kessel (ANW-GK) clustering algorithm was developed for the fault diagnosis of bearings. First, the DCAEN that is pretrained layer by layer by unlabeled samples and fine-tuned by a few labeled samples is applied to learn representative features from the vibration signals. Then, the learned representative features are reduced by t-distributed stochastic neighbor embedding (t-SNE), and the low-dimensional main features are obtained. Finally, the low-dimensional features are input ANW-GK clustering for fault identification. Two datasets were used to validate the effectiveness of the proposed method. The experimental results show that the proposed method can effectively diagnose different fault types with only a few labeled samples. [ABSTRACT FROM AUTHOR]