A novel fault diagnosis method is proposed, depending on a cloud service, for the typical faults in the hydraulic directional valve. The method, based on the Machine Learning Service (MLS) HUAWEI CLOUD, achieves accurate diagnosis of hydraulic valve faults by combining both the advantages of Principal Component Analysis (PCA) in dimensionality reduction and the eXtreme Gradient Boosting (XGBoost) algorithm. First, to obtain the principal component feature set of the pressure signal, PCA was utilized to reduce the dimension of the measured inlet and outlet pressure signals of the hydraulic directional valve. Second, a machine learning sample was constructed by replacing the original fault set with the principal component feature set. Third, the MLS was employed to create an XGBoost model to diagnose valve faults. Lastly, based on model evaluation indicators such as precision, the recall rate, and the F1 score, a test set was used to compare the XGBoost model with the Classification And Regression Trees (CART) model and the Random Forests (RFs) model, respectively. The research results indicate that the proposed method can effectively identify valve faults in the hydraulic directional valve and have higher fault diagnosis accuracy.

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.

In real-world industrial settings, obtaining class-balanced fault data is often difficult. Imbalanced data across categories can degrade diagnostic accuracy. Time-series Generative Adversarial Network (TimeGAN) is an effective tool for addressing one-dimensional data imbalance; however, when dealing with multiple fault categories, it faces issues such as unstable training processes and uncontrollable generation states. To address this issue, from the perspective of data augmentation and classification, a gradient-penalized Conditional Time-series Generative Adversarial Network with a Multi-Scale Importance-aware Network (CTGAN-MSIN) is proposed in this paper. Firstly, a gradient-penalized Conditional Time-Series Generative Adversarial Network (CTGAN) is designed to alleviate data imbalance by controllably generating high-quality fault samples. Secondly, a Multi-scale Importance-aware Network (MSIN) is constructed for fault classification. The MSIN consists of the Multi-scale Depthwise Separable Residual (MDSR) and Scale Enhanced Local Attention (SELA): the MDSR network can efficiently extract multi-scale features, while the SELA network is capable of screening out the most discriminative scale features from them. Finally, the proposed method is validated using the HUST bearing dataset and the axial piston pump dataset. The results show that under the data imbalance ratio of 15:1, the CTGAN-MSIN achieves diagnostic accuracies of 98.75% and 96.50%, respectively, on the two datasets and outperforms the comparison methods under different imbalance ratios.