Friction is an inherent nonlinear disturbance that can lead to creeping, jitter, and decreased tracking precision in an electro-hydraulic servo system. In this paper, the LuGre friction model is used to describe the dynamic and static characteristics of the friction force of a servo system comprehensively. Accurate identification of model parameters is key to implementing friction compensation. However, traditional genetic identification algorithms have the shortcomings of a premature solution, slow convergence, and poor accuracy. To address these shortcomings, this paper proposes an improved adaptive genetic identification algorithm. The proposed algorithm selects evolutionary processes adaptively according to the population concentration in the initial stage of population evolution. Moreover, it adjusts the crossover probability and the mutation probability to identify a local optimum accurately and converge to the global optimum rapidly. During the late stage of population evolution, the accuracy of the global optimal solution can be improved by reducing the search range of identification parameters. The simulation results show that the relative error of the model parameter values identified by the proposed algorithm is reduced to less than 1% and the convergence speed is faster. Compared with the existing traditional genetic algorithm and adaptive genetic algorithm, the overall performance of the proposed method is better. This study provides a feasible and highly accurate identification method for parameter identification of friction models used in electro-hydraulic servo systems.

Mechanical equipment failure may cause massive economic and even life loss. Therefore, the diagnosis of the failures of machine parts in time is crucial. The rolling bearings are one of the most valuable parts, which have attracted the focus of fault diagnosis. Many successful rolling bearing fault diagnoses have been made based on machine learning and deep learning. However, most diagnosis methods still rely on complex signal processing and artificial features, bringing many costs to the deployment and migration of diagnostic models. This paper proposes an end-to-end continuous/discontinuous feature fusion method for rolling bearing fault diagnosis (C/D-FUSA). This method comprises long short-term memory (LSTM), convolutional neural networks (CNN) and attention mechanism, which automatically extracts the continuous and discontinuous features from vibration signals for fault diagnosis. We also propose a contextual-dependent attention module for the LSTM layers. We compare the method with the other simpler deep learning methods and state-of-the-art methods in rolling bearing fault data sets with different sample rates. The results show that our method is more accurate than the other methods with real-time inference. It is also easy to be deployed and trained in a new environment.

The internal gear pump is simple in structure, small in size and light in weight. It is an important basic component that supports the development of hydraulic system with low noise. However, its working environment is harsh and complex, and there are hidden risks related to reliability and exposure of acoustic characteristics over the long term. In order to meet the requirements of reliability and low noise, it is very necessary to make models with strong theoretical value and practical significant to accurately monitor health and predict the remaininglife of the internal gear pump. This paper proposed a multi-channel internal gear pump health status management model based on Robust-ResNet. Robust-ResNet is an optimized ResNet model based on a step factor $h$ in the Eulerian approach to enhance the robustness of the ResNet model. This model was a two-stage deep learning model that classified the current health status of internal gear pumps, and also predicted the remaining useful life (RUL) of internal gear pumps. The model was tested in an internal gear pump dataset collected by the authors. The model was also proven to be useful in the rolling bearing data from Case Western Reserve University (CWRU). The accuracy results of health status classification model were 99.96% and 99.94% in the two datasets. The accuracy of RUL prediction stage in the self-collected dataset was 99.53%. The results demonstrated that the proposed model achieved the best performance compared to other deep learning models and previous studies. The proposed method was also proven to have high inference speed; it could also achieve real-time monitoring of gear health management. This paper provides an extremely effective deep learning model for internal gear pump health management with great application value.

Based on the principle of symmetry, we propose a variable step-size FxLMS algorithm with double nonlinear functions cooperative coupling (DNVSS-FxLMS), aiming to optimize the contradiction between convergence rate and steady-state error in the active pressure pulsation control system of hydraulic systems. The algorithm innovatively couples two types of nonlinear mechanisms (rational-fractional and exponential-function-based), constructing a refined error-step mapping relationship to achieve a balance between rapid convergence and low steady-state error. Simulation experiments were conducted considering the complex time-varying operating environment of a simulation-based hydraulic system. The results demonstrate that, when the system undergoes unstable random changes, the DNVSS-FxLMS algorithm converges at least twice as fast as traditional and existing variable step size algorithms, while reducing steady-state error by 2–5 dB. The proposed DNVSS-FxLMS algorithm exhibits significant advantages in convergence rate, steady-state error reduction, and tracking capability, providing a highly efficient and robust solution for real-time active control of hydraulic system pressure pulsation under complex operating conditions.

The fluid–structure interaction effect should not be disregarded when examining the vibration characteristics of hydraulic pipeline systems. The transfer matrix method (TMM) is an efficacious method for analyzing the vibration characteristics of hydraulic pipelines in the frequency domain, offering advantages such as simplicity and efficiency. However, the TMM suffers the problem of high frequency instability when dealing with long-span hydraulic pipelines, which restricts its practical application. Currently, several modified transfer matrix methods face challenges such as low computational efficiency and difficulties in handling complex boundaries. In response to these issues, this paper proposes a novel modified transfer matrix method known as the mixed variable transfer matrix method. This innovative method possesses clear physical significance and effectively prevents the transfer matrix from becoming singular without necessitating the subdivision of the pipeline length. Consequently, it addresses high-frequency instability while maintaining high computational efficiency. Moreover, this method is capable of addressing complex boundary problems by integrating boundary matrices, thereby demonstrating enhanced applicability compared to existing methods. The performance of the proposed method was validated through the utilization of classic Dubee pipeline impact test data, and the result shows maximum errors of 3.03% relative to the public data. Subsequently, an experiment was conducted on a section of hydraulic piping within a ship’s steering system. A hydraulic fluid noise generator was established to induce fluid pulsation excitation to the pipeline, thereby simulating the actual boundary conditions encountered in a ship’s hydraulic pipeline system so as to corroborate the efficacy of the proposed method in predicting the frequency domain vibration characteristics of a real hydraulic pipeline system. The experimental results indicate that the proposed method offers significant advantages in terms of high precision, efficiency, and stability, shows maximum errors of 4.35% relative to experimental data, and demonstrates promising prospects for engineering applications.