Sensors fault diagnosis of hydraulic automatic gauge control system based on neural network optimized by genetic algorithm

In strip rolling, hydraulic automatic gauge control (HAGC) system is the key element to guarantee the precision of strip gauge. The stability of the kernel pressure closed loop (PCL) in the HAGC system plays an essential role in guaranteeing the rolling process with high performance. Nevertheless, there is some difficulty in exploring the instability mechanism of the HAGC system due to the fact that the PCL is a representative nonlinear closed‐loop control system. In this work, for each component of the HAGC system, the mathematical model was established. And on the basis of the linking relation of various elements, we derived the incremental transfer model of the PCL system. Furthermore, in accordance with the deduced information transfer relation, the transfer block diagram of disturbing variable of the PCL system was obtained. Moreover, for the purpose of deriving the instability condition of the PCL system, the Popov frequency criterion was employed. The instability conditions of the HAGC system were obtained under PCL control. Furthermore, the derived instability conditions of the HAGC system were experimentally verified under various working conditions. The research results provide a fundamental foundation for studying the instability mechanism of the HAGC system.

Hydraulic systems are widely used in modern industries, and the study of efficient and accurate fault diagnosis techniques for hydraulic systems can help prevent accidents and reduce economic losses. Most of the existing deep learning-based intelligent diagnosis methods use only single-channel signals for diagnosis, which may ignore some important fault information. In addition, the existing deep learning algorithms cannot distinguish the sensitivity of inter-channel features, and the important features of the channel domain cannot be emphasized. To solve the above problems, this paper proposes a multi-channel data-driven framework integrated distinct ResNet networks with channel-attention mechanism (CM-ResNet) for fault diagnosis of hydraulic systems. Firstly, the multi-channel signals are converted into 2-dimensional feature maps by continuous wavelet transform. Then, multiple CM-ResNet are trained as base-learners based on the signals of each channel. Third, the category probabilities of each base-learner are concatenated into new feature vectors. Finally, the new feature vectors are used to train the meta-learner and perform fault diagnosis. The meta-learner can capture the complex nonlinear relationships between the base-learners and obtain a stronger learner. Experimental results show that the performance of our proposed method outperforms the comparison method and the already existing methods.

The vibration signal of hydraulic system contains abundant state information, so vibration testing technology is an effective way to realize the fault diagnosis of hydraulic system. However, the mapping relation between signal characteristic and system state is complex and the expression meaning of characteristic is obscure, which brings a great challenge to the hydraulic fault diagnosis. The DBN, a newly proposed deep learning model, has an advantage of autonomously learning and reasoning. And it is good at studying the concealed representation of data and highlighting the feature expression. So, it is contributive to deal with the problems of large capacity data like high dimension, redundancy, and nonlinear etc. Therefore, DBN is chosen as the fault diagnosis method in this paper. Meanwhile, given that the difficulty in feature extraction of hydraulic vibration signal and the important influence of input feature vector to the diagnosing of DBN, a fast and effectively feature extraction method based on sliding-window spectrum feature (SWSF) is proposed. It is effective in remaining the integrity of feature, avoiding the risking of relative shifting of characteristic spectrum, and decreasing the dimensions of feature vector. The experimental results demonstrate that the combination of SWSF and DBN is a fast and effective approach to realize the fault diagnosis of hydraulic system.

Hot strips are presently being rolled out with considerable thickness accuracy. This has been made possible due to superordinated control system comprising of adaptive setup model, monitor thickness control, automatic gauge control, tension and looper control, hydraulic gap control etc. The present work is, however, confined to modeling and simulation studies of hydraulic gap control system in the last stand of the finishig mill of a typical hot strip mill (HSM). The simulation results based on the model show that the system operates effectively to provide high thickness accuracy and good response behavior.

In order to study the thermal effect and noise of cavitation in hydraulic cone type throttle valve, a cone type throttle valve cavitation experiment platform is built. With the organic glass having a better transparency as the material, the model valve was made. The pressure characteristics of five kinds of spool case cone type throttle valves were analyzed through simulation and experimental study, and the main production parts of cone type throttle valve cavitation bubble were obtained. The results show that the frequency of cone type throttle valve noise is above 8kHz. On this basis, we can determine whether the cone type throttle valve noise is the main source of noise in hydraulic system by measurement of noise spectrum. Based on the above findings, it is concluded that it is helpful for fault diagnosis of hydraulic system. The cavitation has relatively great impact on the hydraulic oil temperature flowing through the throttle valve, and its impact cannot be ignored.

The hydraulic load-sensing system is a more efficient system solution than constant pressure system in mobile machine. However, the overall system efficiency for multiple actuators is still low since this centralized hydraulic control system only adapts to the highest load pressure. Therefore, in this article, a distributed hydraulic control system where each function is powered by a hydraulic free piston engine is proposed. Compared with centralized solution, it is more efficient since the power of hydraulic free piston engine is tailored to each function. By integrating small inertia linear engine and pump piston in one unit, the hydraulic free piston engine is an efficient and fast response hydraulic power source. Since the hydraulic free piston engine is essentially a digital hydraulic power source, a digital load-sensing control is proposed in this study. The system architecture, modeling, controller design and performance of the digital hydraulic load-sensing control system are presented. A test bench is developed to verify the system feasibility and performance.

Abstract-Because the request to the quality of cold rolling sheet strips is higher and higher, the hydraulic AGC (Automatic Gauge Control) has been a requisite means for improving the quality of cold rolling strips' finished products. But the PID control strategy, being comprehensively used in the locale, is difficult to satisfy the request of improving the control precision of cold rolling mill, because robustness is not very strong. The dynamic matrix control, brought forward by Cutler, is one of predictive control theories. It adopts the non-parameter model based the step response of the controlled object, and adopts strategies of multi-step prediction, receding-horizon optimization and feedback correction, which has the good tracking characteristic. The dynamic matrix control has succeeded to be applied in the industry field, so it is indispensable and feasible to explore its application in the hydraulic AGC system of the cold rolling mill. But the step response of the whole hydraulic AGC can't be gained easily in the practical project, so it restricts the application of the dynamic matrix control in the hydraulic AGC. In this article, using for reference the thought of the cascade control, the predictive control is combined in the cascade structure. In the thickness loop of the hydraulic AGC, it adopts PID control. And in the inner loop of the screw-down servo system, it adopts the dynamic matrix control. The cascade predictive control in this article solves the problem of gaining the step response of the whole hydraulic AGC difficultly. When adopting the dynamic matrix control, the state observer is constructed, which makes the control system have the state feedback form with the observer, strengthens robustness of the system by correcting the predictive model, and makes the system have the intelligence. It synthesizes merits of PID control, which possesses of the easy algorithm and adjusting the parameter conveniently, the characteristic of the dynamic matrix control including of good tracking characteristic and strong robustness. The result of the experiment shows that the adopted control strategy makes the dynamic quality of the system improved largely. The response time and the control precision of the screw-down servo system are improved. And the thickness precision of the strip is improved too.

The thickness of the steel strip is an important indicator of the overall strip quality. Deviations in thickness are primarily controlled using the automatic gauge control (AGC) system of each rolling stand. At the last stand, the monitoring AGC system is usually used, where the deviations in thickness can be directly measured by the X-ray thickness gauge device and used as the input to the AGC system. However, due to the physical distance between the thickness detection device and the rolling stand, time delay is unavoidably present in the thickness control loop, which can affect control performance and lead to system oscillations. Furthermore, the parameters of the system can change due to perturbations from external disturbances. Therefore, this paper proposes an identification and control scheme for monitoring AGC system that can handle time delay and parameter uncertainty. The cross-correlation function is used to estimate the time delay of the system, while the system parameters are identified using a recursive least squares method. The time delay and parameter estimates are then further refined using the Levenberg-Marquardt algorithm, so as to provide the most accurate parameter estimates for the complete system. Simulation results show that, compared with the standard Proportion Integration Differentiation (PID) controller approach, the proposed approach is not affected by changes in the time delay and parameter uncertainties.

The characteristics of hydraulic automatic gage control System(AGC) of cold rolling mill is analyzed. the principle of identification using Radial Basis Function(RBF) network is introduced. The RBF network structure, train algorithm are given in detail. Finally, the nonlinear model of HAGC is established with RBF network. To improving the accuracy of identification. the input and output data is filtered with wavelet denoising technique. The experimental results indicate that the combination of wavelet denoising and RBF network can provide an efficient method for system identification.

In this paper, we research and develop a new machine called large highland hydraulic intelligent pesticide spraying machine. The pesticide spraying machine adopts hydraulic electric control technology, and adapts to all tall or short crops for pesticide spraying. The machine employs large intelligent computer control technology, and adjusts the spraying dose according to the pest density measured by intelligent sensors. Furthermore, the machine adopts the technology of self-filter and dilutes the pesticide with absorption water automatically. This paper presents hydraulic control system, and puts forward the hydraulic control principle of the walking part, the hydraulic control principle of the work part and main technical parameters.

As a core system of the aircraft, the stability, response speed, and efficiency of the hydraulic system are directly linked to flight safety, control performance, and operational reliability. Hydraulic system failures can lead to catastrophic accidents. This paper proposes a diagnostic method for civil aircraft hydraulic systems based on Quick Access Recorder (QAR) data. First, based on guidance from an airline's maintenance records, eight typical hydraulic system faults were identified. Next, under the guidance of the hydraulic system's working principles, 28 sensor parameters most relevant to hydraulic system faults were selected from over 3,000 parameters collected by the aircraft's sensors. Subsequently, using preprocessed QAR fault data, an improved deep learning model based on Multi-Scale Convolution (MSC) was developed to diagnose hydraulic system faults. The model incorporates an enhanced MSC design, which employs cross-channel convolution on top of multi-scale convolution and integrates a multi-head attention mechanism, enabling the model to extract multi-dimensional features from QAR data more quickly and accurately. Experimental results on the QAR fault dataset demonstrate high prediction accuracy and fast diagnostic speed, achieving an accuracy of 98.2%. This model effectively and accurately diagnoses hydraulic system faults, contributing to flight safety and improving maintenance efficiency.

This paper uses a particle swarm optimization (PSO) algorithm, an adaptive weighted PSO (AWPSO) algorithm, and a genetic algorithm (GA) to determine the optimal proportional-integral-derivative controller’s parameters of a hydraulic position control system. A typical hydraulic servo system has been selected as an application. The mathematical model of this hydraulic servo system which comprises the most relevant dynamics and nonlinear effects is considered. The model simulates the behavior of a REXROTH servo valve and includes the nonlinearities of friction forces, valve dynamics, oil compressibility, and load influence. The performance indices, which have been used in the optimization process, are integral absolute error, integral square error and integral time absolute error. The proposed controller is implemented on the simulation model to identify the best method for tuning the controller. Compared with GA and AWPSO results, the PSO method has been found to be more efficient and robust in improving the step response of a position control for hydraulic systems in terms of settling time, maximum overshoot and undershoot.

To reduce longitudinal gauge deviations of the head sections at the hot strip mill rolling it is reasonable to profile band of variable thickness due to the length provided by the linear motion of the screw-down arrangements under load. The paper considers the structure of the automatic gauge control system (ACGS) of the indirect type. It estimates the accuracy of screw-down arrangement position control in the single-loop automatic position control system. It also justifies the use of automatic position combined control system. The introduction of the additional channel of control due to the first derivative of the reference input signal was proved to provide the required accuracy values of the strip gauge control. The paper presents data of the pilot study of the developed system at OJSC Magnitogorsk Iron and Steel Works' mill 2,000; they confirm its efficiency.

To enhance the competence of the product in construction machinery, a proper testing system should be established for the construction machinery hydraulic system. Considering multi-site test and massive control of data processing in most construction machinery hydraulic measurement and control system, a testing platform applicable to different construction hydraulic systems is designed and presented in this paper. Composed of the upper and lower computers, the system is simple and convenient by means of Virtual Instrument Technology. Then it runs very well and proves reliable after debugging operation. Finally, the test-control platform can set manifold experiments in different working conditions.

The application of construction machinery is an important technical condition to ensure the development of industrialization. The stability of its equipment operation is a necessary condition to maintain industrial production efficiency. In the application of construction machinery, the use of hydraulic system is more frequent, because the hydraulic system in the long-term use often appear various problems, which needs to strengthen the fault diagnosis and maintenance technology of the hydraulic system of construction machinery, in order to make it in a good working state. This paper introduces the working characteristics and fault types of hydraulic system of construction machinery, lists the diagnosis methods of hydraulic system fault, and puts forward the specific application of hydraulic system fault maintenance technology.