Transient acceleration waveform replication of electro-hydraulic systems using convex combined adaptive controller with input shaping technique.

Electro-hydraulic systems are extensively utilized to generate desired acceleration waveforms to provide a vibration environment for testing the performance and reliability of objects in various industrial applications. However, as electro-hydraulic systems are often affected by some inevitable drawbacks resulted from hydraulic nonlinearities, unwanted dynamic variations and disturbances, the generated acceleration waveform is generally far behind the expectation. In this paper, a convex combined adaptive controller with input shaping technique is proposed for enhancing the transient acceleration waveform replication accuracy of electro-hydraulic systems. The proposed controller is comprised of a three variable controller at the bottom level, an input shaping technique controller at the middle level, and a convex combined adaptive controller at the upper level. The three variable controller is firstly utilized for the establishment of a fundamental closed-loop acceleration control system, and then the input shaping technique controller is constructed by introducing an offline designed inverse prefilter utilizing the multi-innovation recursive least squares algorithm and the zero magnitude error tracking algorithm. The convex combined adaptive controller at the upper level is comprised of two individual adaptive filters with high and low step sizes, which provides the merits of fast convergence rate and high tracking accuracy, and it is further exploited to address for system's dynamic variations, model uncertainties and unexpected perturbations. Comparative experiments of the proposed controller with a manually generated random waveform and a recorded earthquake waveform as the testing inputs are conducted on a typical electro-hydraulic test bench, and the corresponding results demonstrate the feasibility and superiority of the proposed controller in improving the transient acceleration waveform replication performance of electro-hydraulic systems.

Nonlinear model predictive control-Cross-coupling control with deep neural network feedforward for multi-hydraulic system synchronization control.

This paper studies a multi-hydraulic system (MHS) synchronization control algorithm. Firstly, a general nonlinear asymmetric MHS state space entirety model is established and subsequently the model form is simplified by nonlinear feedback linearization. Secondly, an entirety model-type solution is proposed, integrating a nonlinear model predictive control (NMPC) algorithm with a cross-coupling control (CCC) algorithm. Furthermore, a novel disturbance compensator based on the system's inverse model is introduced to effectively handle disturbances, encompassing unmodeled errors and noise. The proposed innovative controller, known as nonlinear model predictive control-cross-coupling control with deep neural network feedforward (NMPC-CCC-DNNF), is designed to minimize synchronization errors and counteract the impact of disturbances. The stability of the control system is rigorously demonstrated. Finally, simulation results underscore the efficacy of the NMPC-CCC-DNNF controller, showcasing a remarkable 60.8% reduction in synchronization root mean square error (RMSE) compared to other controllers, reaching up to 91.1% in various simulations. These results affirm the superior control performance achieved by the NMPC-CCC-DNNF controller.

Fault Diagnosis of Hydraulic Components Based on Multi-Sensor Information Fusion Using Improved TSO-CNN-BiLSTM.

In order to realize the accurate and reliable fault diagnosis of hydraulic systems, a diagnostic model based on improved tuna swarm optimization (ITSO), optimized convolutional neural networks (CNNs), and bi-directional long short-term memory (BiLSTM) networks is proposed. Firstly, sensor selection is implemented using the random forest algorithm to select useful signals from six kinds of physical or virtual sensors including pressure, temperature, flow rate, vibration, motor power, and motor efficiency coefficient. After that, fused features are extracted by CNN, and then, BiLSTM is applied to learn the forward and backward information contained in the data. The ITSO algorithm is adopted to adaptively optimize the learning rate, regularization coefficient, and node number to obtain the optimal CNN-BiLSTM network. Improved Chebyshev chaotic mapping and the nonlinear reduction strategy are adopted to improve population initialization and individual position updating, further promoting the optimization effect of TSO. The experimental results show that the proposed method can automatically extract fusion features and effectively utilize multi-sensor information. The diagnostic accuracies of the plunger pump, cooler, throttle valve, and accumulator are 99.07%, 99.4%, 98.81%, and 98.51%, respectively. The diagnostic results of noisy data with 0 dB, 5 dB, and 10 dB signal-to-noise ratios (SNRs) show that the ITSO-CNN-BiLSTM model has good robustness to noise interference.

Real-Time Fault Diagnosis for Hydraulic System Based on Multi-Sensor Convolutional Neural Network.

This paper proposed a real-time fault diagnostic method for hydraulic systems using data collected from multiple sensors. The method is based on a proposed multi-sensor convolutional neural network (MS-CNN) that incorporates feature extraction, sensor selection, and fault diagnosis into an end-to-end model. Both the sensor selection process and fault diagnosis process are based on abstract fault-related features learned by a CNN deep learning model. Therefore, compared with the traditional sensor-and-feature selection method, the proposed MS-CNN can find the sensor channels containing higher-level fault-related features, which provides two advantages for diagnosis. First, the sensor selection can reduce the redundant information and improve the diagnostic performance of the model. Secondly, the reduced number of sensors simplifies the model, reducing communication burden and computational complexity. These two advantages make the MS-CNN suitable for real-time hydraulic system fault diagnosis, in which the multi-sensor feature extraction and the computation speed are both significant. The proposed MS-CNN approach is evaluated experimentally on an electric-hydraulic subsea control system test rig and an open-source dataset. The proposed method shows obvious superiority in terms of both diagnosis accuracy and computational speed when compared with traditional CNN models and other state-of-the-art multi-sensor diagnostic methods.

Active fault-tolerant control for the dual-valve hydraulic system with unknown dead-zone.

This paper proposes a method for high-performance motion control of the dual-valve hydraulic system subject to parameter and model uncertainties, unknown proportional valve dead-zone, and servo valve fault. By constructing a detailed dual-valve fault system model (DFSM), a disturbance observer-based adaptive robust fault-tolerant controller is proposed via the backstepping method. This controller integrates a model-based fault detection algorithm for real-time fault monitoring and subsequent controller reconfiguration. Additionally, the DFSM-based adaptive robust control (ARC) technique is applied to handle the unknown dead-zone problem and other nonlinearities, ensuring precise control. Once the servo valve fault occurs, a nonlinear observer estimates the fault and collaborates with the ARC to establish a reconfigured controller, thereby maintaining motion control. The effectiveness of the proposed method has been experimentally verified.

Incipient Fault Detection in a Hydraulic System Using Canonical Variable Analysis Combined with Adaptive Kernel Density Estimation.

Incipient fault detection in a hydraulic system is a challenge in the condition monitoring community. Existing research mainly monitors abnormal working conditions in hydraulic systems by separately detecting the key working parameter, which often causes a high miss warning rate for incipient faults due to the oversight of parameter dependence. A principal component analysis provides an effective method for incipient fault detection by taking the correlation of multiple parameters into consideration, but this technique assumes the systems are Gaussian-distributed, making it invalid for a dynamic non-Gaussian system. In this paper, we combine a canonical variable analysis (CVA) and adaptive kernel density estimation (AKDE) for the early fault detection of nonlinear dynamic hydraulic systems. The collected hydraulic system data set was used to construct the typical variable space, and the state space and residual space are divided to represent the characteristics of different correlations between the two variables, which are quantitatively described using Hotelling's T2 and Q. In order to investigate the proper upper control limits, AKDE was utilised to estimate the underlying probability density functions of T2 and Q by taking the nonlinearity of the hydraulic system variables into consideration. The advantages of the proposed approach for incipient fault detection are illustrated via a marine power plant lubrication system.

RISE-based adaptive control of electro-hydraulic servo system with uncertain compensation.

Electro-hydraulic servo system (EHSS) plays an important role in many industrial and military applications. However, its high-performance tracking control is still a challenging mission due to its nonlinear system dynamics and model uncertainties. In this paper, a novel adaptive robust integral method of the sign of the error (ARISE) with extended state observer (ESO) is proposed. Firstly, the nonlinear mathematical model of typical EHSS with modeling uncurtains and uncertain nonlinear is established. Then, ESO is used to estimate the state and lumped disturbance, of which the unknown parameter estimations can be updated by the novel adaptive law. Results shows that the novel controller achieves better tracking performance in maximum tracking error, average tracking error and standard deviation of the tracking error.

Intelligent PID control of an industrial electro-hydraulic system.

In this study, an intelligent PID (i-PID) controller is designed for position control of a nonlinear electro-hydraulic system with uncertain valve characteristics and supply pressure variations. The proposed controller uses estimation of ultra-local model of the system. To exhibit the capability of the proposed method, the controller parameters are optimized via the particle swarm optimization method through a nominal nonlinear model of the system. Then, the performance of the i-PID controller, parameters of which are optimized by using the nominal model, is examined under uncertainties caused by valve characteristics and supply pressure variations. Moreover, the friction between the piston and the hydraulic cylinder is also considered in experiments. A PID controller whose parameters are also optimized based on the same performance criteria, is used for comparison purposes with i-PID control both in simulations and experiments. Performance metrics of the controllers are examined and presented by employing two separate reference signals: Sine wave and ramp. The results show that the i-PID controller shows significantly better results than the classical PID controller in tracking the test signals under various supply pressures and valve modes.

Optimizing Fat Grafting Using a Hydraulic System Technique for Fat Processing: A Time and Cost Analysis.

Background Many authors have researched ways to optimize fat grafting by looking for a technique that offers safe and long-term fat survival rate. To date, there is no standardized protocol. We designed a "hydraulic system technique" optimizing the relationship among the quantity of injected fat, operative time, and material cost to establish fat volume cutoffs for a single procedure. Methods Thirty-six patients underwent fat grafting surgery and were organized into three groups according to material used: standard, "1-track," and "2-tracks" systems. The amount of harvested and grafted fat as well as material used for each procedure was collected. Operating times were recorded and statistical analysis was performed to establish the relationship with the amount of treated fat. Results In 15 cases the standard system was used (mean treated fat 72 [30-100] mL, mean cost 4.23 ± 0.27 euros), in 11 cases the "1-track" system (mean treated fat 183.3 [120-280] mL, mean cost 7.63 ± 0.6 euros), and in 10 cases the "2-tracks" one (mean treated fat 311[220-550] mL, mean cost 12.47 ± 1 euros). The mean time difference between the standard system and the "1-track" system is statistically significant starting from three fat syringes (90 mL) in 17.66 versus 6.87 minutes. The difference between the "1-track" system and "2-tracks" system becomes statistically significant from 240 mL of fat in 15 minutes ("1-track") versus 9.3 minutes for the "2-tracks" system. Conclusion Data analysis would indicate the use of the standard system, "1-track," and "2-tracks" to treat an amount of fat < 90 mL of fat, 90 ÷ 240 mL of fat, and ≥ 240 mL of fat, respectively.

Dynamic Modeling and Experimental Validation of a Water Hydraulic Soft Manipulator Based on an Improved Newton-Euler Iterative Method.

Compared with rigid robots, soft robots have better adaptability to the environment because of their pliability. However, due to the lower structural stiffness of the soft manipulator, the posture of the manipulator is usually decided by the weight and the external load under operating conditions. Therefore, it is necessary to conduct dynamics modeling and movement analysis of the soft manipulator. In this paper, a fabric reinforced soft manipulator driven by a water hydraulic system is firstly proposed, and the dynamics of both the soft manipulator and hydraulic system are considered. Specifically, a dynamic model of the soft manipulator is established based on an improved Newton-Euler iterative method, which comprehensively considers the influence of inertial force, elastic force, damping force, as well as combined bending and torsion moments. The dynamics of the water hydraulic system consider the effects of cylinder inertia, friction, and water response. Finally, the accuracy of the proposed dynamic model is verified by comparing the simulation results with the experimental data about the steady and dynamic characteristics of the soft manipulator under various conditions. The results show that the maximum sectional error is about 0.0245 m and that the maximum cumulative error is 0.042 m, which validate the effectiveness of the proposed model.

Research on Control Method of the Power System of Stepping-Type Anchoring Equipment.

To improve the roadway adaptability and control accuracy of anchoring equipment, a stepping anchoring device was designed. A permanent-magnet synchronous motor control and a harmonic suppression algorithm were integrated to optimize the dynamic control system of stepping-type anchoring equipment. The results of an experimental simulation and analysis showed that when the coefficient of coal rock hardness f = 5, 6, and 7, the pulsation coefficient of the hydraulic pump outlet pressure, hydraulic motor output speed, and pump-controlled hydraulic cylinder advance speed in the hydraulic circuit of a pump-controlled motor did not exceed 3% after the equipment based on sliding mode control (SMC) entered the steady state, while the maximum pulsation coefficient was only 32.5% of the PI control. Based on the SMC, the harmonic components of the permanent magnet synchronous motor in the power system were suppressed and compensated for. This enhanced the stiffness of the hydraulic system under motor drive. When the rock stiffness factor gradually changed from f = 5 to f = 8 and increased suddenly from f = 5 to f = 6, the pressure overshoot at the outlet of the hydraulic pump of the pump-controlled motor system was reduced from 11.19% to 7.97% and from 61.19% to 52.88%, respectively, compared with that before the optimization. It was thereby proven that SMC based on harmonic suppression can effectively reduce the system pulsation caused by the multi-factor coupling of anchoring equipment and provide technical support for the optimal control of the power system of stepping-type anchoring equipment.

Estimating the Characteristic Curve of a Directional Control Valve in a Combined Multibody and Hydraulic System Using an Augmented Discrete Extended Kalman Filter.

The estimation of the parameters of a simulation model such that the model's behaviour matches closely with reality can be a cumbersome task. This is due to the fact that a number of model parameters cannot be directly measured, and such parameters might change during the course of operation in a real system. Friction between different machine components is one example of these parameters. This can be due to a number of reasons, such as wear. Nevertheless, if one is able to accurately define all necessary parameters, essential information about the performance of the system machinery can be acquired. This information can be, in turn, utilised for product-specific tuning or predictive maintenance. To estimate parameters, the augmented discrete extended Kalman filter with a curve fitting method can be used, as demonstrated in this paper. In this study, the proposed estimation algorithm is applied to estimate the characteristic curves of a directional control valve in a four-bar mechanism actuated by a fluid power system. The mechanism is modelled by using the double-step semi-recursive multibody formulation, whereas the fluid power system under study is modelled by employing the lumped fluid theory. In practise, the characteristic curves of a directional control valve is described by three to six data control points of a third-order B-spline curve in the augmented discrete extended Kalman filter. The results demonstrate that the highly non-linear unknown characteristic curves can be estimated by using the proposed parameter estimation algorithm. It is also demonstrated that the root mean square error associated with the estimation of the characteristic curve is 0.08% with respect to the real model. In addition, all the errors in the estimated states and parameters of the system are within the 95% confidence interval. The estimation of the characteristic curve in a hydraulic valve can provide essential information for performance monitoring and maintenance applications.

Output feedback adaptive super-twisting sliding mode control of hydraulic systems with disturbance compensation.

This paper presents an output feedback adaptive super-twisting sliding mode controller (SSMC) for hydraulic systems with unmodeled disturbances via utilizing an extended state observer (ESO). Both unmeasured system states and unmodeled disturbances are estimated by ESO based on output position signal, which avoids using noise-polluted signals and eliminates most of the disturbance effects on control performance simultaneously. Moreover, a SSMC is developed to further suppress the residual error of disturbance compensation, in which feedback gains are adapted online to further reduce the high-gain feedback. In addition, this proposed controller is continuous and chattering-free, which is beneficial to practical applications. Theoretical analysis indicates that the proposed controller ensures an asymptotic stability when existing constant disturbances, and ultimately bounded tracking performance for the time-variant disturbance case. Comparative experimental results reveal the validity of the developed approach.

Impact of operational temperature changes and freeze-thaw cycles on the hydraulic conductivity of borehole heat exchangers.

A large share of the primary energy is consumed to provide space heating. Geothermal energy offers a regenerative alternative. For reasons of efficiency and environmental protection, it is important to ensure the system integrity of a borehole heat exchanger (BHE). Previous investigations have focused on the individual components of the BHE or on the grout and pipe systems' integrity. This study focused on the analysis of the hydraulic system integrity of the complete subsoil-grout-pipe system as well as possible thermally induced changes. For this purpose, a pilot-scale experiment was built to test a 1-m section of a typical BHE under in situ pressure, hydraulic and temperature conditions. During the tests the hydraulic system permeability of the soil and the BHE was measured continuously and separately from each other. In addition, the temperature monitoring array was installed in a 50-cm cross-sectional area. Significant temperature-related fluctuations in the sealing performance could be observed. Hydraulic conductivity limits required by VDI 4640-2 (Thermal use of the underground-ground source heat pump systems, 2019) were exceeded without frost action. The succeeding application of freeze-thaw cycles further enhances the system permeability. The study shows that the thermally induced effects on the system integrity of the BHE are larger and more significant than the subsequent frost-induced effects. The hydrophobic character of the high-density polyethylene (PE-HD) pipes as well as its high coefficient of thermal expansion seem to be the main points of weakness in the system. Optimization research should focus on the interface connection between grout and pipe, whereby hydrophilic pipe materials such as stainless steel or aluminum should also be considered as well as manipulation of the pipe surface properties of PE-HD.

Real-Time Monitoring for Hydraulic States Based on Convolutional Bidirectional LSTM with Attention Mechanism.

By monitoring a hydraulic system using artificial intelligence, we can detect anomalous data in a manufacturing workshop. In addition, by analyzing the anomalous data, we can diagnose faults and prevent failures. However, artificial intelligence, especially deep learning, needs to learn much data, and it is often difficult to get enough data at the real manufacturing site. In this paper, we apply augmentation to increase the amount of data. In addition, we propose real-time monitoring based on a deep-learning model that uses convergence of a convolutional neural network (CNN), a bidirectional long short-term memory network (BiLSTM), and an attention mechanism. CNN extracts features from input data, and BiLSTM learns feature information. The learned information is then fed to the sigmoid classifier to find out if it is normal or abnormal. Experimental results show that the proposed model works better than other deep-learning models, such as CNN or long short-term memory (LSTM).

Efficacy of water filters for dental chair units: assessment of the filtration action versus Coxsackievirus B5.

INTRODUCTION: The microbiological safety and control of the water used in dental practice has a critical importance for avoiding cross-linked infections in the dental office. The aim of this study was to establish coxsackie virus filtration of the water applied to a dental unit. METHODS: A specific water filter-system was used, to verify the viral load in the outgoing water. The statistical analysis was performedusing the Shapiro-Wilk and t-Student test. RESULTS: The outcome of the evaluation of the virologic tests shows an excellent capability of virus filtration that attested 99.9999% in the volume analyzed. A statistical difference was found in the bacterial water contamination parameter before and after filtration. (P = 0.000000). CONCLUSIONS: According to the tests, medical devices applied to a dental unit are able to filter viruses and therefore reduce risk of contamination in the dental office.

Analysis of Weak Fault in Hydraulic System Based on Multi-scale Permutation Entropy of Fault-Sensitive Intrinsic Mode Function and Deep Belief Network.

With the aim of automatic recognition of weak faults in hydraulic systems, this paper proposes an identification method based on multi-scale permutation entropy feature extraction of fault-sensitive intrinsic mode function (IMF) and deep belief network (DBN). In this method, the leakage fault signal is first decomposed by empirical mode decomposition (EMD), and fault-sensitive IMF components are screened by adopting the correlation analysis method. The multi-scale entropy feature of each screened IMF is then extracted and features closely related to the weak fault information are then obtained. Finally, DBN is used for identification of fault diagnosis. Experimental results prove that this identification method has an ideal recognition effect. It can accurately judge whether there is a leakage fault, determine the degree of severity of the fault, and can diagnose and analyze hydraulic weak faults in general.

Design and Experimental Research of a Miniature Digital Hydraulic Valve.

A digital hydraulic valve is an important component of the digital hydraulic system, and its performance is directly related to the system function. In order to make the valve system more competitive in dimension, digital valve miniaturization is an important research point. A new micro digital valve is designed, which is analyzed from the mechanical structure and magnetic circuit mechanism, and the design difficulties are also expounded. The four subsystems and switching characteristics of the valve are theoretically analyzed and simulated. In order to test the performance of the valve, a test system is designed, and performance of the new micro valve is tested. The test results show that the switch characteristic analysis of the valve is correct. The comparison between the test curve and the simulation curve is carried out, which demonstrates that the accuracy of the simulation model is reasonable. The theoretical analysis of the new micro digital valve is consistent with experiments.

Fractional-Order PID Control Strategy on Hydraulic-Loading System of Typical Electromechanical Platform.

In this paper, a control method for a hydraulic loading system of an electromechanical platform based on a fractional-order PID (Proportion-Integration-Differentiation) controller is proposed, which is used to drive the loading system of a mechatronic journal test rig. The mathematical model of the control system is established according to the principle of the electro-hydraulic system. Considering the indetermination of model parameters, the method of parameter identification was used to verify the rationality of the theoretical model. In order to improve the control precision of the hydraulic loading system, the traditional PID controller and fractional-order PID controller are designed by selecting appropriate tuning parameters. Their control performances are analyzed in frequency domain and time domain, respectively. The results show that the fractional-order PID controller has better control effect. By observing the actual control effect of the fractional-order PID controller on the journal test rig, the effectiveness of this control algorithm is verified.

Earliest hydraulic enterprise in China, 5,100 years ago.

Here we present one of the world's oldest examples of large-scale and formalized water management, in the case of the Liangzhu culture of the Yangtze Delta, dated at 5,300-4,300 years cal B.P. The Liangzhu culture represented a peak of early cultural and social development predating the historically recorded Chinese dynasties; hence, this study reveals more about the ancient origins of hydraulic engineering as a core element of social, political, and economic developments. Archaeological surveys and excavations can now portray the impressive extent and structure of dams, levees, ditches, and other landscape-transforming features, supporting the ancient city of Liangzhu, with an estimated size of about 300 ha. The results indicate an enormous collective undertaking, with unprecedented evidence for understanding how the city, economy, and society of Liangzhu functioned and developed at such a large scale. Concurrent with the evidence of technological achievements and economic success, a unique relationship between ritual order and social power is seen in the renowned jade objects in Liangzhu elite burials, thus expanding our view beyond the practicalities of water management and rice farming.