Abstract
Because deep belief networks (DBNs) in deep learning have a powerful ability to extract useful information from the raw data without prior knowledge, DBNs are used to extract the useful feature from the roller bearings vibration signals. Unlike classification methods, the clustering method can classify the different fault types without data label. Therefore, a method based on deep belief networks (DBNs) in deep learning (DL) and fuzzy C‐means (FCM) clustering algorithm for roller bearings fault diagnosis without a data label is presented in this paper. Firstly, the roller bearings vibration signals are extracted by using DBN, and then principal component analysis (PCA) is used to reduce the dimension of the vibration signal features. Secondly, the first two principal components (PCs) are selected as the input of fuzzy C‐means (FCM) for roller bearings fault identification. Finally, the experimental results show that the fault diagnosis of the method presented is better than that of other combination models, such as variation mode decomposition‐ (VMD‐) singular value decomposition‐ (SVD‐) FCM, and ensemble empirical mode decomposition‐ (EEMD‐) fuzzy entropy‐ (FE‐) PCA‐FCM.
Highlights
With the development of science and technology, aerospace equipment, industrial equipment, and other fields of mechanical and electrical equipment have become increasingly complex, intelligent, and integrated, so that the operating conditions and the working environment are becoming more complex and changeable. erefore, accurate and effective fault diagnosis in complex equipment systems becomes an effective way to improve the reliability and safety of the systems and to reduce the maintenance cost [1]
The principal component analysis (PCA) is used to reduce the dimension of the former eigenvectors, and the first two principal components (PCs) are selected as the input of fuzzy C-means (FCM) to fulfill the roller bearings fault diagnosis
E BF3 and ORF3 samples in Figures 8(a)–8(e) using the empirical mode decomposition- (EEMD-)PCA-FCM and variation mode decomposition- (VMD-)singular value decomposition- (SVD-)FCM models are scattered randomly, but in Figures 8(f )–8(h), these scattered data points are more concentrated at one point, and the data of different fault types are more separated. is demonstrates that the deep belief networks (DBNs) has a good feature extraction ability
Summary
With the development of science and technology, aerospace equipment, industrial equipment, and other fields of mechanical and electrical equipment have become increasingly complex, intelligent, and integrated, so that the operating conditions and the working environment are becoming more complex and changeable. erefore, accurate and effective fault diagnosis in complex equipment systems becomes an effective way to improve the reliability and safety of the systems and to reduce the maintenance cost [1]. As the vibration signals have nonlinear and nonstationary features, this is not a self-adaptive method To overcome this drawback, empirical mode decomposition. Ensemble empirical mode decomposition (EEMD) [9] can solve the mode-mixing problem self-adaptively by introducing Gaussian white noise and decomposing a complicated signal into IMFs. Many scholars use the EEMD and entropy combination models to extract the vibration signal features. Zhang and Zhou employed the EEMD to decompose the roller bearings’ vibration signals into some IMFs, and fuzzy entropy (EE) is used to calculate the IMF entropy values; the extracted features are selected as the input of the support vector machine (SVM) for roller bearing fault diagnosis [10]. 2.2. eoretical Framework of PCA. e essence of PCA is to retain the coordinates of the main components as w(1)
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