Since the hydraulic axial piston pump is the engine that drives hydraulic transmission systems, it is widely utilized in aerospace, marine equipment, civil engineering, and mechanical engineering. Operating safely and dependably is crucial, and failure poses a major risk. Hydraulic axial piston pump malfunctions are characterized by internal concealment, challenging self-adaptive feature extraction, and blatant timing of fault signals. By completely integrating the time-frequency feature conversion capability of synchrosqueezing wavelet transform (SWT), the feature extraction capability of VGG11, as well as the feature memory capability of the long short-term memory (LSTM) model, a novel intelligent fault identification method is proposed in this paper. First, the status data are transformed into two dimensions in terms of time and frequency by using SWT. Second, the depth features of the time–frequency map are obtained and dimensionality reduction is carried out by using the deep feature mining capability of VGG11. Third, LSTM is added to provide the damage identification model for long-term memory capabilities. The Softmax layer is utilized for the intelligent evaluation of various damage patterns and health state. The proposed method is utilized to identify and diagnose five typical states, including normal state, swash plate wear, sliding slipper wear, loose slipper, and center spring failure, based on the externally observed vibration signals of a hydraulic axial piston pump. The results indicate that the average test accuracy for five typical state signals reaches 99.43%, the standard deviation is 0.0011, and the average test duration is 2.675 s. The integrated model exhibits improved all-around performance when compared to LSTM, LeNet-5, AlexNet, VGG11, and other typical models. The proposed method is validated to be efficient and accurate for the intelligent identification of common defects of hydraulic axial piston pumps.

The piston pump is the significant source of motive force in a hydraulic transmission system. Owing to the changeable working conditions and complex structural characteristics, multiple friction pairs in the piston pump are prone to wear and failure. An accurate fault diagnosis method is a crucial guarantee for system reliability. Deep learning provides a great insight into the intelligent exploration of machinery fault diagnosis. Hyperparameters are very important to construct an effective deep model with good performance. This research fully mines the feature component from vibration signals, and converts the failure recognition into a classification issue via establishing a deep model. Furthermore, Bayesian algorithm is introduced for hyperparameter optimization as it considers prior information. An adaptive convolutional neural network is established for typical failure pattern recognition of an axial piston pump. The proposed method can automatically complete fault classification and represents a higher accuracy by experimental verification. Typical failures of an axial piston pump are intelligently diagnosed with reduced subjectivity and preprocessing knowledge. The proposed method achieves an identification accuracy of more than 98% for five typical conditions of an axial piston pump.

Hydraulic axial piston pumps are the power source of fluid power systems and have important applications in many fields. They have a compact structure, high efficiency, large transmission power, and excellent flow variable performance. However, the crucial components of pumps easily suffer from different faults. It is therefore important to investigate a precise fault identification method to maintain reliability of the system. The use of deep models in feature learning, data mining, automatic identification, and classification has led to the development of novel fault diagnosis methods. In this research, typical faults and wears of the important friction pairs of piston pumps were analyzed. Different working conditions were considered by monitoring outlet pressure signals. To overcome the low efficiency and time-consuming nature of traditional manual parameter tuning, the Bayesian algorithm was introduced for adaptive optimization of an established deep learning model. The proposed method can explore potential fault feature information from the signals and adaptively identify the main fault types. The average diagnostic accuracy was found to reach up to 100%, indicating the ability of the method to detect typical faults of axial piston pumps with high precision.

Hydraulic transmission and control technology is a core supporting technology for agricultural machinery. Its technical performance directly determines the operation quality, production efficiency, and overall reliability of agricultural machinery. This paper focuses on hydraulic transmission and control technology for agricultural machinery. It systematically reviews the field from three dimensions: key technology analysis, research status review, and development trend outlook. First, it elaborates the three core technical systems in agricultural machinery hydraulics. In terms of hydraulic components, it analyzes the working principles and application scenarios of power elements, actuating elements, and control elements. In terms of hydraulic systems, it analyzes the technical characteristics and evolution paths of five typical systems used in agricultural machinery. In terms of control technology, it summarizes a multi-level technical framework. This framework includes classical PID and its improved algorithms, advanced control methods, and intelligent control methods. Second, based on the latest domestic and international research literature, it comprehensively reviews the research progress in hydraulic components, hydraulic systems, and control methods. Finally, in response to the transformation and upgrading of smart agriculture and the “dual carbon” strategy, it identifies three major future directions for hydraulic transmission and control technology in agricultural machinery. The findings of this review are expected to offer theoretical references and technical guidance for the R&D and industrial upgrading of high-end hydraulic systems for agricultural machinery.

Fluid components and systems are major components of modern mechanical equipment and have been widely used in various fields such as engineering machinery, rotating machinery, and hydraulic machinery

Against the backdrop of growing global water scarcity and mounting food security pressures, the intelligent transformation of agricultural irrigation and drainage machinery is crucial for enhancing operational efficiency, reducing energy consumption, and ensuring reliable field operations. This paper systematically reviews the evolution of agricultural irrigation and drainage machinery from traditional manual operation and mechanized practices to automated control and intelligent decision-making. Within a closed-loop “sensing–decision-making–execution” framework, particular emphasis is placed on key technologies, including multisource sensing, intelligent decision-making, adaptive actuation, and precision control. The findings indicate that multiscale, multimodal information fusion enhances the observability of farmland environments and equipment conditions. Irrigation and drainage decision-making is shifting from timer-based, threshold-based, and empirical-rule-based approaches toward state prediction, mechanism-constrained control, and multi-objective optimization. Precision irrigation is advancing from state monitoring to predictive decision-making and closed-loop intelligent control. However, there are still key challenges such as data heterogeneity, insufficient cross-scenario model generalizability, and limitations in model interpretability and real-time performance. Future research should focus on integrated irrigation and drainage, multimodal spatiotemporal fusion, mechanism and data collaborative driving, cloud–edge–end collaborative intelligence, and standardized field verification to enhance the autonomous decision-making, precision operation, and long-term reliability of agricultural irrigation and drainage machinery in complex environments.