The field of mechanical fault diagnosis has entered the era of “big data”. However, existing diagnostic algorithms, relying on artificial feature extraction and expert knowledge are of poor extraction ability and lack self-adaptability in the mass data. In the fault diagnosis of rotating machinery, due to the accidental occurrence of equipment faults, the proportion of fault samples is small, the samples are imbalanced, and available data are scarce, which leads to the low accuracy rate of the intelligent diagnosis model trained to identify the equipment state. To solve the above problems, an end-to-end diagnosis model is first proposed, which is an intelligent fault diagnosis method based on one-dimensional convolutional neural network (1D-CNN). That is to say, the original vibration signal is directly input into the model for identification. After that, through combining the convolutional neural network with the generative adversarial networks, a data expansion method based on the one-dimensional deep convolutional generative adversarial networks (1D-DCGAN) is constructed to generate small sample size fault samples and construct the balanced data set. Meanwhile, in order to solve the problem that the network is difficult to optimize, gradient penalty and Wasserstein distance are introduced. Through the test of bearing database and hydraulic pump, it shows that the one-dimensional convolution operation has strong feature extraction ability for vibration signals. The proposed method is very accurate for fault diagnosis of the two kinds of equipment, and high-quality expansion of the original data can be achieved.

Applications in industrial production have indicated that the challenges of sparse fault samples and singular monitoring data will diminish the performance of deep learning-based diagnostic models to varying degrees. To alleviate the above issues, a multi-source information fusion meta-learning network with convolutional block attention module (CBAM) is proposed in this study for bearing fault diagnosis under limited dataset. This method can fully extract and exploit the complementary and enriched fault-related features in the multi-source monitoring data through the designed multi-branch fusion structure and incorporate metric-based meta-learning to enhance the fault diagnosis performance of the model under limited data samples. Furthermore, the introduction of CBAM can further assist the model to trade-off and focus on more discriminative information in both spatial and channel dimensions. Extensive experiments conducted on two bearing datasets that cover multi-source monitoring data fully demonstrate the validity and superiority of the proposed method.

To address the problem that the faults in axial piston pumps are complex and difficult to effectively diagnose, an integrated hydraulic pump fault diagnosis method based on the modified ensemble empirical mode decomposition (MEEMD), autoregressive (AR) spectrum energy, and wavelet kernel extreme learning machine (WKELM) methods is presented in this paper. First, the non-linear and non-stationary hydraulic pump vibration signals are decomposed into several intrinsic mode function (IMF) components by the MEEMD method. Next, AR spectrum analysis is performed for each IMF component, in order to extract the AR spectrum energy of each component as fault characteristics. Then, a hydraulic pump fault diagnosis model based on WKELM is built, in order to extract the features and diagnose faults of hydraulic pump vibration signals, for which the recognition accuracy reached 100%. Finally, the fault diagnosis effect of the hydraulic pump fault diagnosis method proposed in this paper is compared with BP neural network, support vector machine (SVM), and extreme learning machine (ELM) methods. The hydraulic pump fault diagnosis method presented in this paper can diagnose faults of single slipper wear, single slipper loosing and center spring wear type with 100% accuracy, and the fault diagnosis time is only 0.002 s. The results demonstrate that the integrated hydraulic pump fault diagnosis method based on MEEMD, AR spectrum, and WKELM methods has higher fault recognition accuracy and faster speed than existing alternatives.

Hydraulic pumps are commonly used; however, it is difficult to predict their remaining useful life (RUL) effectively. A new method based on kernel principal component analysis (KPCA) and the just in time learning (JITL) method was proposed to solve this problem. First, as the research object, the non-substitute time tac-tail life experiment pressure signals of gear pumps were collected. Following the removal and denoising of the DC component of the pressure signals by the wavelet packet method, multiple characteristic indices were extracted. Subsequently, the KPCA method was used to calculate the weighted fusion of the selected feature indices. Then the state evaluation indices were extracted to characterize the performance degradation of the gear pumps. Finally, an RUL prediction method based on the k-vector nearest neighbor (k-VNN) and JITL methods was proposed. The k-VNN method refers to both the Euclidean distance and angle relationship between two vectors as the basis for modeling. The prediction results verified the feasibility and effectiveness of the proposed method. Compared to the traditional JITL RUL prediction method based on the k-nearest neighbor algorithm, the proposed prediction model of the RUL of a gear pump presents a higher prediction accuracy. The method proposed in this paper is expected to be applied to the RUL prediction and condition monitoring and has broad application prospects and wide applicability.

This paper addresses the difficulty of evaluating operating status in widely used gear pumps. A method for constructing hydraulic pump health indicators and evaluating health status is proposed based on LSTM–VAE. In this study, the vibration signal data source of gear pumps was assessed in the accelerated life test. Firstly, the normalized feature vectors of the whole-life operation data of gear pumps were extracted by wavelet packet decomposition and amplitude feature extraction. Combining an LSTM algorithm with a VAE algorithm, a method for constructing hydraulic pump health indicators based on LSTM–VAE is proposed. By learning the feature vectors of gear pumps in varying health conditions, a one-dimensional HI curve of the gear pumps was obtained. Then, LSTM was used to predict the HI curve of gear pumps. According to the volume efficiency of the gear pumps, the health status of gear pumps is divided into four states: health, sub-health, deterioration, and failure. The health status of the hydraulic pump is accurately evaluated by the health indicator. Finally, the proposed method is compared with the traditional method based on feature selection and PCA dimensionality reduction. The health indicator constructed by the method proposed in this paper is superior to the traditional method in terms of tendency, robustness, and monotonicity, which proves the effectiveness of the method proposed in this paper.