Dynamic Prediction of Performance Degradation Characteristics of Direct-Drive Electro-Hydraulic Servo Valves

Mi, Juncheng; Guo-qin, Huang

doi:10.3390/app13127231

Cited by 4 publications

(9 citation statements)

References 25 publications

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“…In previous studies [ 18 , 19 ], it was found that direct-drive electro-hydraulic servo valves are mainly affected by erosive wear and the degree of wear is different because of different openings, differential pressures, and oil contamination. In the first stage of the experiment, pressure gain and leakage were selected as the performance parameters of the servo valve and thus the exponential smoothing and ARIMA model algorithms were chosen for prediction and analysis.…”

Section: Experimental Design and Data Analysis Of Performance Degrada...mentioning

confidence: 99%

“…Figure 1 shows the schematic diagram of the servo valve spool and sleeve erosion and wear experimental system, as well as the original experiments in the same testing platform and servo valves [ 19 ]; solid throttle valves, globe valves, pressure gauges, and other equipment will not be elaborated on. Hydraulic pump 2 is the main pump of the whole flow control system and is responsible for the supply of the working medium or hydraulic oil.…”

Section: Experimental Design and Data Analysis Of Performance Degrada...mentioning

confidence: 99%

“…We used the same setup as a previous study [ 19 ], and the oil contamination, opening degree, and differential pressure were still used as the orthogonal setup parameters for the experimental conditions. In the previous study [ 18 ], the erosive wear was explored as the main wear of the servo valve spool and sleeve, with opening degree, differential pressure, and contamination degree being the main influencing factors.…”

Section: Experimental Design and Data Analysis Of Performance Degrada...mentioning

confidence: 99%

“…The experimental data for pressure gain and leakage volume, as well as the error index calculation theory and the previous research literature [ 19 ], were the same, while the LSTM prediction algorithm model established in this paper obtained a lower MAE and MAPE than those obtained in the previous literature using the exponential smoothing algorithm model and ARIMA algorithm model, respectively: the MAE for the pressure gain was 2.21 × 10 − 4 MPa/mm, MAPE of pressure gain was 1.33 × 10 − 4 , MAE of leakage was 6.436 × 10 − 5 L/min, and MAPE of leakage was 1.58 × 10 − 4 . It can be seen that the LSTM prediction model was better than the exponential smoothing algorithm and ARIMA algorithm model for predicting servo valves in the aerospace field; this has been shown in previous research literature.…”

Section: Lifespan Predictionmentioning

confidence: 99%

“…Secondly, the use of big data for neural network predictive analysis, because it is based on data obtained from the actual application of servo valves and its predictive effect is closer to the real value and the actual situation than other purely theoretical analyses, can be considered to be directly referenced by the aerospace field. This paper combines the author’s previous research [ 18 , 19 ] to design and improve the performance degradation experiment of servo valves using pressure difference, opening, and oil contamination as the pre-influence conditions. Experimental data are obtained through long-term experiments, and a life prediction model is constructed based on the performance parameter data obtained from the investigation combined with neural networks.…”

Section: Introductionmentioning

confidence: 99%

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Direct-Drive Electro-Hydraulic Servo Valve Performance Characteristics Prediction Based on Big Data and Neural Networks

Huang

2023

Sensors

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Direct-drive electro-hydraulic servo valves play a key role in aerospace control systems, and their operational stability and safety reliability are crucial to the safety, stability, and efficiency of the entire control system. Based on the prediction of the performance change of the servo valve and the resulting judgement and prediction of its life, this can effectively avoid serious accidents and economic losses caused by failure due to performance degradation in the work. On the basis of existing research, factors such as opening, oil contamination, and pressure difference are used as prerequisites for the operation of direct-drive electro-hydraulic servo valves. In addition to the current research on pressure gain and leakage, the performance parameters of servo valves, such as overlap, threshold, and symmetry, are also expanded and selected as research objects, combined with pressure design servo valve performance degradation experiments for testing instruments such as flow and position sensors, and data are obtained on changes in various performance parameters. The experimental data are analyzed and a prediction model is built to predict the performance parameters of the servo valve by combining the existing popular neural networks, and the prediction error is calculated to verify the accuracy and validity of the model. The experimental results indicate that as the working time progresses, the degree of erosion and wear on the valve core and valve sleeve of the servo valve increases. Overall, it has been observed that the performance parameters of the servo valve show a slow trend of change under different working conditions, and the rate of change is generally higher under high pollution (level 9) conditions than under other conditions. The prediction results indicate that the predicted values of various performance parameters of the servo valve by the prediction model are lower than 0.2% compared to the experimental test set data. By comparing the two dimensions of the accuracy and prediction trend, this model meets industrial needs and outperforms deep learning algorithm models such as the exponential smoothing algorithm and ARIMA model. The experiments and results of this study provide theoretical support for the life prediction model of servo valves based on neural networks and machine learning in artificial intelligence, and provide a reference for the development of direct-drive electro-hydraulic servo valves in aerospace and other industrial fields for use and failure standards.

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Section: Experimental Design and Data Analysis Of Performance Degrada...mentioning

confidence: 99%

Section: Experimental Design and Data Analysis Of Performance Degrada...mentioning

confidence: 99%

Section: Experimental Design and Data Analysis Of Performance Degrada...mentioning

confidence: 99%

Section: Lifespan Predictionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Direct-Drive Electro-Hydraulic Servo Valve Performance Characteristics Prediction Based on Big Data and Neural Networks

Huang

2023

Sensors

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Analysis of Vibration Characteristics of Electro-hydraulic Driven 3-UPS/S Parallel Stabilization Platform

Yuan,

Wang,

Pang

et al. 2024

Chin. J. Mech. Eng.

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With the development of fluid-power transmission and control technology, electro-hydraulic-driven technology can significantly improve the load-carrying capacity, stiffness, and control accuracy of stabilization platforms. However, compared with mechanically driven platforms, the stiffness and damping of the fluid, as well as the coupling effect between the fluid and the structure need to be considered for electro-hydraulic-driven parallel stabilization platforms, making the modal and dynamic response characteristics of the mechanism more complex. With the aim of solving the aforementioned issues, we research the electro-hydraulic driven 3-UPS/S parallel stabilization platform considering the hinge stiffness. Moreover, the characteristic vibration equation of the mechanism is established using the virtual work principle. Subsequently, the variation characteristics of the natural frequency and the vibration response according to the position of the mechanism are analyzed based on the dynamic equation. Finally, the correctness of the model is verified by a modal test and Runge-Kutta methods. This study provides a theoretical basis for the dynamic design of electrohydraulic-driven parallel mechanisms.

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Rainfall, Wind Speed, and Temperature Forecast Using Triple Exponential Smoothing and Gradient Descent

Kusuma Hakim,

Gernowo,

Widhi Nirwansyah

2024

KSS

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The global community strives to minimize the impact of disasters through various actions, for example, mapping flood-prone areas. Flood-prone areas need to be identified correctly, predicted, understood, and socialized to minimize risks when a disaster occurs regarding death, property damage, and socio-economic losses. This type of data-based prediction has been developed and implemented widely and can be applied to predictions related to hydrology. Data mining approaches (estimation, classification, clustering, and time-series forecast) have significantly influenced research related to flood prediction in recent years. The time-series flood forecast has been widely used in previous research using various statistical and data-mining methods. Predicting floods that occur in coastal areas is less discussed than river floods. One method that is often used is exponential smoothing. Determining damping factor values (alpha, beta, and gamma) in the triple exponential smoothing method, in general, is to use all values from 0 to 1 to find the most optimal damping factor, this takes quite a long time and results generally appear with less accuracy. So, a combination of the triple exponential smoothing algorithm is proposed to perform tTimeseries forecast, and the gradient descent algorithm is used as an optimization algorithm to obtain optimal weight values for alpha, beta, and gamma in triple exponential smoothing. Keywords: triple exponential smoothing, gradient descent, flood forecast, flood prediction, time-series forecast

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Dynamic Prediction of Performance Degradation Characteristics of Direct-Drive Electro-Hydraulic Servo Valves

Cited by 4 publications

References 25 publications

Direct-Drive Electro-Hydraulic Servo Valve Performance Characteristics Prediction Based on Big Data and Neural Networks

Direct-Drive Electro-Hydraulic Servo Valve Performance Characteristics Prediction Based on Big Data and Neural Networks

Analysis of Vibration Characteristics of Electro-hydraulic Driven 3-UPS/S Parallel Stabilization Platform

Rainfall, Wind Speed, and Temperature Forecast Using Triple Exponential Smoothing and Gradient Descent

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