Components Of Hydraulic System Research Articles

Assessment of Hydraulic Oil Properties during Operation of a Mini Loader

This study employs a rigorous methodology to assess the condition of the hydraulic oil in a recently introduced mini skid-steer loader. The assessment is conducted through laboratory analysis, which adheres to a range of international standards. The objective is to provide accurate insights into the viscosity, particle count, and characterisation of the oil, along with thermographic data. The friction of oil is evaluated at specific time points: 0, 10, 100, 125, and 150 h of operation. This examination offers a comprehensive insight into the alterations in oil characteristics during a pivotal period when machine components are undergoing initial consolidation and abrasion to attain the factory-defined performance thresholds. The principal aim of this research is to provide valuable insights into the wear of oil and hydraulic system components through an in-depth analysis of a range of variables. Moreover, the investigation aims to ascertain the impact of this factor on the temperature elevation of system components and accessories to formulate enhanced technical guidelines for implementation. The main results indicate the presence of particles in the oil, resulting in a cleanliness code of 23/21/13, which exceeds the permissible threshold of 20/18/15 specified in ISO 11171. In addition, hydraulic oil shows a viscosity instability of more than 10% due to moisture absorption, leading to wear of mechanical components composed of iron, nickel, copper, zinc, and silicon. This deterioration is corroborated by thermographic evaluations, which reveal a considerable temperature increase in components such as cylinders and system accessories.

A hybrid intelligent diagnostic approach for spool jamming faults of hydraulic directional valves

Directional valves are critical functional components in hydraulic systems for diverting the flow of hydraulic oil and controlling oil pressure. Faults of the valves could lead to failure and even serious accidents in hydraulic systems. Fault diagnostic accuracy is directly influenced by the effectiveness of signal processing. Due to the harsh operating environment of hydraulic systems, the condition monitoring signal acquired from the systems contains a large amount of redundant information and environmental noise. This makes the training processes of fault diagnostic approaches time-consuming and inefficient. In this paper, a new hybrid intelligent approach to spool jamming fault diagnostics for hydraulic directional valves is presented. The approach is designed based on the combined strengths of wavelet packet decomposition (WPD), empirical mode decomposition (EMD), variable mode decomposition (VMD), and the multi-layer perceptron (MLP) to improve the efficiency and accuracy of the fault diagnostic process. Five evaluation indices, including processing time (PT), local average accuracy (LAA), stability of accuracy (SA), weighted average of the neuron number (WANN), and robustness of WANN (ROB), are applied to benchmark the approach using ablation experiments. The experimental results show that the hybrid approach has a strong capability to extract fault-related features from the signal and perform fault diagnostics with high accuracy, efficiency, and robustness.

A fault diagnosis method for hydraulic system based on multi-branch neural networks

As a key part of construction machinery, the hydraulic system is widely used in industrial fields. Due to its complexity and high integration, the fault diagnosis of hydraulic systems has always been a challenging problem. However, existing methods or models mainly focus on individual hydraulic system components while overlooking the interactions between different components, which are limited to specific application scenarios. Therefore, this research aims to develop a multi-task network model to diagnose faults in multiple components of hydraulic systems. Firstly, the initial data was collected from the hydraulic system test bench, in which the extreme gradient boosting (XGBoost) was introduced to evaluate the importance of all features under different fault types. Then, reduction dimensionality is achieved by setting a threshold for feature selection. Subsequently, a novel multi-branch deep neural network (MBDNN), which utilizes multiple branches to extract different information and correlations in the input data, is proposed and established. The multi-level combination of residual block and multi-branch connections enables MBDNN to achieve multiple types of fault diagnosis simultaneously and compensate for the problem of insufficient information representation in single-branch neural networks. The results of multiple rounds of experimental indicate that the MBDNN has higher robustness and accuracy in hydraulic system fault diagnosis than the existing general methods, and the diagnostic accuracy of multi-type faults is improved to 98.5%.

A Review of State of the Art for Accelerated Testing in Fluid Power Pitch Systems

Failures in hydraulic systems in offshore wind turbines represent an enormous challenge for manufacturers and operators, as the pitch system statistically is one of the subsystems contributing the most to the downtime of the turbines, which is the case for both electrical and hydraulic pitch systems. However, the complex failure mechanisms of the various different hydraulic components mean that, typically, the critical components of hydraulic systems must be tested to better understand the failure mechanisms. Nonetheless, conventional testing procedures are lengthy and costly. Accelerated testing plays a critical role as it can mimic hydraulic system failure mechanisms in a shorter period. However, the lack of standardized test methods and detailed knowledge about the failure-accelerating effects complicates the process. Therefore, this paper offers a comprehensive examination of approaches applicable to conducting accelerated tests on hydraulic systems. It identifies and discusses five primary component types or sub-components related to the acceleration of testing in hydraulic systems: pumps, cylinders, seals, valves, and hoses. Each section references studies that delve into accelerated testing methodologies for these individual components. Furthermore, within each component, a concise overview of the current techniques is provided, followed by a discussion and summary based on the state of the art.

Prediction and Dynamic Simulation Verification of Output Characteristics of Radial Piston Motors Based on Neural Networks

Radial piston motors are executive components in hydraulic systems, tasked with providing appropriate torque and speed according to load requirements in practical applications. The purpose of this study is to predict the output torque of radial piston hydraulic motors and confirm their suitable operating conditions. Efficiency determination experiments were conducted on physical models, yielding thirty sets of performance data. Torque (output torque) and mechanical efficiency from the experimental data were selected as prediction targets and fitted using two methods: multiple linear regression and neural networks. A dynamic simulation model was built using Adams2020 software to obtain theoretical torque values, enabling the verification of the alignment between the predicted values and simulation results. The results indicate that the error between the theoretical torque of the dynamic model and the physical experiments is 1.9%, with the error of the neural network predictions being within 2%. The dynamic simulation model can yield highly accurate theoretical torque values, providing a reference for the external load of hydraulic motors; additionally, neural networks offer accurate predictions of output torque, thus reducing experimental testing costs.

Structure optimization design approach of friction pairs for low-temperature rise wet brake in hydraulic motor

Cam-lobe radial-piston hydraulic motors are widely used in large heavy-duty machinery as direct-drive components of hydraulic systems. Due to their extremely high output torque and power density, the wet brake equipped with the hydraulic motors are necessary to achieve ultra-high braking torque in a compact space. However, due to a large number of internal friction pairs and small fit clearance between multiple friction pairs in wet brake, the rotation of the friction pairs in non-braking state will cause large friction torque loss and obvious temperature rise, thereby leading to the thermal failure of wet brake. How to optimize the structure of friction pairs to reduce the temperature rise of wet brake in non-braking state is a persistent challenge. Existing structure optimization design approaches of friction pairs in brakes mainly focus on the structure of each friction pair, which ignore the effect of fit clearance between the friction pairs on the temperature rise of brakes. Nevertheless, the fit clearance between friction pairs is an important factor that affect the temperature rise. For that, this study proposes a two-step structure optimization design approach of friction pairs for low-temperature rise wet brake in hydraculic motor, which simultaneously considers the fit clearance between the friction pairs and the structure of each friction pair. In this approach, the design process could be divided into two steps: firstly, the influence of fit clearance between multiple friction pairs on the temperature rise of wet brake in non-braking state is analyzed and optimized by the temperature rise theoretical model; secondly, in order to further reduce the temperature rise of wet brake, the oil groove structure of each friction plate are introduced and optimized by fluid-solid-thermal coupling simulation method. By this approach, the low-temperature rise structure of friction pairs with the optimized fit clearance between multiple friction pairs and oil grooves of each friction plate. Finally, a series of experiments are conducted to verify the effectiveness of the design approach. The result show that the temperature rise of wet brake could be effectively reduced 12 % by optimizing the structure of wet brake, demonstrating that the structure optimization design approach could provide a theoretical guidance to design low-temperature rise wet brake for large torque hydraulic motors.

Investigation of Valve Seat Cone Angle on Small Opening Direct-Acting Relief Valve Cavitation Noise

Direct-acting relief valves are important pressure-control components in hydraulic systems; however, noise problems are now common. This study aimed to reduce and numerically analyze the valve cavitation and noise using the Zwart–Gerber–Belamri (ZBG) model with the Ffowcs Williams and Hawkings (FW–H) model to optimize the design based on the sound field perspective. First, a direct-acting relief valve flow field model was established to determine the relationship between the seat structure and the degree of cavitation through a CFD (Computational Fluid Dynamics) simulation. Second, sound field analysis was conducted based on the cavitation and non-cavitation flow fields, respectively, and the resulting noise levels were compared. Finally, prototypes of the relief valve were manufactured, and noise levels between the original and optimized valves were compared. The results revealed that cavitation within the relief valve generated noise while optimizing the valve seat cone angle suppressed this phenomenon, thereby reducing the noise emitted by the optimized valve by 18.2 dB compared to the original valve. These findings can serve as a guide for designing and optimizing direct-acting relief valves.

MONITORING OF LUBRICANT OIL OF HYDRAULIC SYSTEMS AS PART OF PREDICTIVE MAINTENANCE

Hydraulic systems are widely used in different industries, such as civil construction, aerospace, automotive, oil, among others. The hydraulic fluid or lubricating oil of these systems is an important source of information regarding the state of the machines. In this way, the analysis of lubricating oil proves to be an important and effective tool to monitor the condition of components of hydraulic systems. Since the emergence of predictive maintenance, researchers have been dedicated to developing methods of online monitoring of hydraulic fluids in order to prevent failures, increase safety and reduce maintenance costs. Therefore, the present work sought to carry out a discussion on the importance of monitoring the hydraulic fluid, bringing the main methods of analysis and the most recent advances.

СИСТЕМА УПРАВЛЕНИЯ БЕЗОПАСНОСТЬЮ ПОЛЁТОВ ЛЕТАТЕЛЬНЫХ АППАРАТОВ С БАЗОВОЙ ВСТРОЕННОЙ СИСТЕМОЙ АВТОМАТИЧЕСКОГО ДОПУСКОВОГО КОНТРОЛЯ ГИДРАВЛИЧЕСКОЙ СИСТЕМЫ И СИСТЕМ МОНИТОРИНГА И ПРОГНОЗИРОВАНИЯ ОТКАЗОВ

This paper considers an integrated tolerance control system with failure monitoring and prediction subsystems for aircraft safety management system (ASMS), which is relevant nowadays. The paper deals with the operational component of hydraulic system (HS) tolerance zones. The internal measured parameter is pressure, so it was necessary to find an algorithm that reflects its dependence on operating conditions, its coefficient of kinematic viscosity of the fluid, its temperature, operating time, ambient temperature and flight stage. The operational component of the tolerance zone can be derived from the obtained expression for the pressure by substituting the boundary values of the parameters included in this dependence. While polling the sensors of the automatic control system it is necessary each time to calculate the range of variation of the parameter for the given stage of flight and other conditions with the help of algorithms for calculating the tolerance zone obtained in the paper. Moreover, the control system has to process a large amount of information using artificial intelligence (AI) methods which allows the safety of aircraft flight (SAF) to be managed.

Penerapan Automatic Particle Counter untuk Pemantauan Kondisi Oli Hidrolik

High solid particle contamination in hydraulic oil can cause abnormal damage to hydraulic system components. Monitoring the hydraulic oil solid particle contamination level is necessary to ensure it is always within permissible limits. Monitoring of solid particle contamination in hydraulic oil is still done manually by taking an oil sample and then measuring the level of solid particle contamination in the oil sample. This method still relies on human power. Therefore, there is still a possibility that oil conditions are not monitored due to human negligence. This research aims to design a system that automatically samples hydraulic oil and measures solid particle contamination. The level of solid particles in the hydraulic oil is measured by the Automatic Particle Counter (APC) sensor, and then the data is sent to the microcontroller. The microcontroller will process this data and provide information regarding the level of solid particles in the hydraulic oil in real-time. This research produces a system that automatically monitors and produces ISO Cleanliness Code values and a warning system when solid particle levels exceed specified limits. Therefore, this tool can help users monitor hydraulic oil conditions more easily.

Anomaly Detection of Axial Piston Pump Based on the DTW-RCK-IF Composite Method Using Pressure Signals

Axial piston pumps are critical components of hydraulic systems due to their compact design and high volumetric efficiency, making them widely used. However, they are prone to failure in harsh environments characterized by high pressure and heavy loads over extended periods. Therefore, detecting abnormal behavior in axial piston pumps is of significant importance. Traditional detection methods often rely on vibration signals from the pump casings; however, these signals are susceptible to external environmental interference. In contrast, pressure signals exhibit greater stability. In this study, we propose a novel anomaly detection method for axial piston pumps, referred to as DTW-RCK-IF, which combines dynamic time warping (DTW) for data segmentation, a random convolutional kernel (RCK) for feature extraction, and isolation forest (IF) for anomaly detection using pressure signals. The model is trained using normal operating data to enable the effective detection of abnormal states. First, the DTW algorithm is employed to segment the raw data, ensuring a high degree of similarity between the segmented data. Next, the random convolutional kernel approach is used in a convolutional neural network for feature extraction, resulting in features that are representative of normal operating conditions. Finally, the isolation forest algorithm calculates the anomaly scores for anomaly detection. Experimental simulations on axial piston pumps demonstrate that, compared with vibration signals, the DTW-RCK-IF approach using pressure signals yields superior results in detecting abnormal data, with an average F1 score of 98.79% and a good fault warning effect. Validation using the publicly available CWRU-bearing and XJTU-SY-bearing full-life datasets further confirms the effectiveness of this method, with average F1 scores of 99.35% and 99.73%, respectively, highlighting its broad applicability and potential for widespread use.

Intelligent predictive maintenance of hydraulic systems based on virtual knowledge graph

In the manufacturing industry, a hydraulic system harnesses liquid fluid power to create powerful machines. Under the trend of Industry 4.0, the predictive maintenance of hydraulic systems is transforming to more intelligent and automated approaches that leverage the strong power of artificial intelligence and data science technologies. However, due to the knowledge-intensive and heterogeneous nature of the manufacturing domain, the data and information required for predictive maintenance are normally collected from ubiquitous sensing networks. This leads to the gap between massive heterogeneous data/information resources in hydraulic system components and the limited cognitive ability of system users. Moreover, how to capture and structure useful domain knowledge (in a machine-readable way) for solving domain-specific tasks remains an open challenge for the predictive maintenance of hydraulic systems. To address these challenges, in this paper we propose a virtual knowledge graph-based approach for the digital modeling and intelligent predictive analytics of hydraulic systems. We evaluate the functionalities and effectiveness of the proposed approach on a predictive maintenance task under real-world industrial contexts. Results show that our proposed approach is capable and feasible to be implemented for digital modeling, data access, data integration, and predictive analytics.

ОБЕСПЕЧЕННОСТЬ РЫНКА СПЕЦИАЛИЗИРОВАННОЙ ТЕХНИКИ ГИДРАВЛИЧЕСКИМИ РАСПРЕДЕЛИТЕЛЯМИ РОССИЙСКОГО ПРОИЗВОДСТВА

Currently, a relevant task for self-propelled machinery manufacturers is to replace hydraulic system components of the leading global manufacturers with hydraulic system components of alternative Russian manufacturers. The price share of a hydraulic system within the production cost of complete machinery is estimated by experts to be 5% to 25%. At the same time in the Russian Federation, hydraulic system components are produced in insignificant volumes. The components produced in the territory of the Russian Federation often fail to meet state-of-the-art requirements; therefore, it is the foreign components that are mainly used. The task of organizing production of state-of-the-art components for hydraulic systems is a very important one and is aimed at provision of production of modern equipment for various purposes in the Russian Federation. This work was carried out in order to assess the possibility of consolidated inquiry arrangement, qualitative development of the existing production of hydraulic equipment and creation of new production facilities. In the work, a list of typical hydraulic distribution equipment used in the modern special-purpose machinery design was compiled, and a dimensional range of hydraulic distribution equipment parameters was justified. Based on the results of analysis of the special-purpose machinery market, forecast market demand for hydraulic distribution equipment by segments until 2030 was determined. In order to assess the hydraulic distribution equipment market for types of machines not included in this study, based on expert opinions, we assume that their market is 50% of the considered types of machines. The analysis showed that when producing special-purpose machinery, hydraulic distribution equipment from various manufacturers is used, and hydraulic distribution equipment is one of the expensive components taking up to 20% of the total cost of a hydraulic system of special-purpose machines. The hydraulic distribution equipment potential market capacity in the Russian Federation in 2030 perspective will amount to 1.34 million pcs for a total of 56.9 billion rubles.

THE WEAR OF THE OMR 100 GEROLLER HYDRAULIC MOTOR PARTS

Sauer Danfoss produces a large range of hydraulic equipment, which is installed on tractors, agricultural and construction equipment of Russian and foreign manufacturers. The hydraulic motor is one of the most frequently encountered components of hydraulic systems of machines. (Research purpose) The research purpose is assessing the technical condition of worn rollers, housing, shaft, sprocket and holder of the Danfoss OMR 100 roller hydraulic motor; to conduct micrometer studies of parts. (Materials and methods) Measurements were carried out using smooth digital micrometers of the SHAN brand with a division price of one micrometer, an ICH-type clock indicator, an electronic lever-type nutrometer of the QLR Research Center with a division price of one micrometer. All parts were degreased before measurement. The working surface of the rollers and sprockets was studied using a magnifier LI-3 10x GOST 25706-83. Measurements were carried out in two perpendicular directions and in several sections. (Results and discussion) It was found that the average wear on the side is: roller - 6 micrometers; seat under the roller - 2; the wear of the shaft hole in the housing is 2.5; the wear of the shaft necks is 1.5; the end surfaces of the sprocket and rollers are 5.5 micrometers. (Conclusions) The working surfaces of the rollers and sprockets have the greatest wear. The values of the average wear of these parts suggest that the method of electric spark treatment will be appropriate to restore worn surfaces. This method guarantees the production of a coating of small thickness with good tribotechnical properties under conditions of waterjet wear.

Algorithm for hydraulic system parameters of a dump truck

At the initial stage of designing a dump truck it is necessary to determine the main units and assemblies of the vehicle, their parameters and dimensions. One of the systems of the dump truck is the hydraulic system. When creating new dump trucks, there is a necessity to define parameters and characteristics of hydraulic system components. The peculiarity of quarry dump trucks, in contrast to dump trucks, which are used on public roads, is that the modes of their operation are different. Therefore, when creating new dump trucks, it is necessary to develop an algorithm for selecting and justifying the parameters of the hydraulic system at the early stages of design. The article substantiates the necessity of developing an algorithm for determining the parameters of the hydraulic system. The main hydraulic consumers are defined and the requirements of each of the consumers are presented. The algorithm of justification of the parameters of the hydraulic system of the quarry dump truck is described. Determination of the required pump capacity of the hydraulic system is described. The use of the described algorithm for justification of the hydraulic system parameters of the dump truck contributes to the reduction of terms of development of design documentation at the early stages of design.

Fault diagnosis and location of hydraulic system of domestic civil aircraft based on logic data

To study the typical fault diagnosis and fault location technology of the hydraulic system of the domestic civil aircraft, the logic data of the typical fault is constructed according to the formation conditions of the fault. The operation data of the typical fault is collected, and the Bayesian network is used to realize the fault diagnosis and fault components position. First, according to the fault formation conditions in the unit operation manual of a certain type of domestic civil aircraft, referring to the construction method of the logic data, taking the typical fault of the hydraulic system as an example, the fault logic data of the domestic civil aircraft is established to intuitively reflect the logical relationship of the fault formation; secondly, based on the constructed logic data, considering the formation conditions of the fault, a Bayesian network corresponding to the logic data is established, and the logical relationship formed by the fault is represented by the value of the conditional probability distribution; obtain quick access recorder (QAR) data and its parameter information according to the input information of the logic data; finally, according to the established Bayesian network and the obtained QAR data, apply forward reasoning to realize the diagnosis of typical faults of the hydraulic system. Under the condition of partial information, reverse reasoning is applied to locate the faulty components of hydraulic system. The research shows that the proposed method can accurately diagnose faults, and can accurately locate faulty components in complete information, and give the probability of occurrence of potentially faulty components under partial information, which can effectively assist in the location of faulty components. The research work has certain reference significance for improving the fault diagnosis function of the airborne health management system and the ground health management system of domestic civil aircraft.

Coincidence of pressure pulsations with excitation of mechanical vibrations of hydraulic system components: An experimental study

This paper discusses certain excitations that affect the components of hydraulic systems: pipes and valves. The negative effects of low-frequency noise and vibration on humans were also pointed out. Particular attention is given to the effect of pulsatile flow on the structure of hydraulic components. Pressure pulsations in a hydraulic system have been shown to generate and transmit mechanical vibrations across a wide spectrum of frequencies, and the negative consequences of this phenomenon have been pointed out. Based on the latest generation of proportional directional control valve, a special stand was built to generate pressure pulsations over a wide frequency range (up to 350 Hz). Amplitude-frequency spectra of mechanical vibrations and pressure pulsations were used instead of time courses in the considerations. The paper concludes that effort must be made to reduce the amplitudes of pressure pulsations in hydraulic systems, particularly in the low-frequency spectrum.

WaterSafe: A Water Network Benchmark for Fault Diagnosis Research

Currently, in the water distribution systems literature, fault detection methods are typically evaluated on benchmark water networks that do not include real-time experimental data, or on private commercial datasets, which prohibit the reproducibility of the results. Moreover, realistic modeling of faults on hydraulic system components, sensors and actuators is often unavailable. In this work, we provide a framework for the application of fault-diagnosis methodologies on WaterSafe, a water network benchmark for fault diagnosis. The WaterSafe benchmark is a small scale replica of a water transport network constructed using industrial components and devices, while the communications are implemented in a way that resemble a water utility's Supervisory Control and Data Acquisition system. A general problem formulation for fault-diagnosis on water systems is provided, in accordance to the mathematical model of the benchmark. Moreover, we provide a calibrated simulation model including system, sensor and actuator faults, based on observations from the real system. Finally, we provide open access to the datasets generated from the experiments containing the aforementioned faults.

Data-Driven Fault Diagnosis in a Complex Hydraulic System based on Early Classification

In this paper, an early time-series classification (ETSC) algorithm is applied to support fault diagnosis in a complex hydraulic system (HS) with several interconnected components. The proposed technique aims at early classifying the state of the system while keeping the loss of classification inaccuracy at the minimum level. In contrast to baseline models that detect the eventual faults at the end of each working cycle, the ETSC model can diagnose any fault type of the HS components before observing the entire working cycle. Indeed, the early classification model successfully achieves a trade-off between the accuracy and the earliness criterion. Experimental results on a realistic HS dataset from the related literature show that the ETSC method can effectively identify different fault types with a higher accuracy and earlier compared to baseline methodologies.

Experimental study of the control of cavitation-induced erosion created by collapse of single bubbles using a micro structured riblet

Cavitation can be formed on ship rudders, ship propellers and hydraulic system components and can induce erosion on solid surfaces. One of the remaining open questions in the study of the cavitation erosion is finding a method to control erosion generated by the collapse of single bubbles near a solid surface. In this study, we proposed a passive control method using a shark skin inspired micro structured riblet solid surface to control the dynamics of a single bubble collapse and the cavitation-induced erosion. We experimentally investigated the effects of a micro structured V-shaped riblets on the dynamics of a laser-generated single cavitation bubble. First, the dynamics of a single cavitation bubble near a smooth rigid surface is obtained at different relative wall distances. Second, the dynamics of a single cavitation bubble near a rigid surface covered by micro structured riblets are compared with our experiment on the smooth rigid surface for the relative wall distances. The results showed that the bubble dynamics collapsing near the riblet surface differed significantly from the single bubble collapse near the smooth surface. The interaction of microbubbles formed between the bubble and the riblet surface during the first collapse mitigated the momentum of the microjet. Furthermore, the riblet structure altered the bubble’s shape during its collapse and rebound process and significantly reduced the torus ring generated after the first and second collapses. The riblet structure also reduced the cavitation-induced erosion area on the rigid surface with the micro structured riblet.