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Advanced Control and Fault Detection Techniques in Hydraulic Machines and...
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Advanced Control and Fault Detection Techniques in Hydraulic Machines and Systems

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machines Testing and Maintenance".

Deadline for manuscript submissions: 1 January 2025 | Viewed by 3351

Special Issue Editor

Dr. Piotr Czop

E-Mail Website
Guest Editor
Department of Robotics and Mechatronics, AGH University of Science and Technology, Kraków, Poland
Interests: methodology; optimization; simulation; modeling; algorithms; heat exchangers; mathematical programming; Heuristics; linear programming; scheduling

Special Issue Information

Dear Colleagues,

Advanced control and fault detection techniques play a vital role in enhancing the performance, efficiency, and reliability of hydraulic machines and systems. With their wide application in various industries, such as manufacturing, construction, aerospace, and automotive, hydraulic systems are crucial for transmitting power and controlling motion. Hydraulic machines and systems play a vital role in various industries, and their optimal operation is crucial for productivity, safety, and cost-effectiveness. However, these systems often face challenges related to nonlinear dynamics, external disturbances, and component failures, leading to decreased efficiency and potential downtime.

The aim of this Special Issue is to bring together cutting-edge research and innovative developments in the field of "Advanced Control and Fault Detection Techniques in Hydraulic Machines and Systems". The primary focus of this Special Issue is to explore novel approaches, methodologies, and technologies that advance the control and fault detection strategies employed in hydraulic systems to achieve superior performance, efficiency, and reliability. This Special Issue aims to address these challenges by soliciting high-quality research contributions that cover, but are not limited to, the following topics:

Advanced Control Techniques:

  • Model-based control strategies for hydraulic machines and systems.
  • Nonlinear control techniques to address complex system dynamics.
  • Robust control methodologies for enhanced system stability and performance.
  • Intelligent control approaches, such as fuzzy logic and neural networks, to improve adaptability and fault tolerance.

Fault Detection and Diagnosis Methods:

  • Model-based fault detection techniques for hydraulic systems.
  • Data-driven approaches using machine learning algorithms for fault detection and diagnosis.
  • Sensor fusion methodologies to improve fault detection accuracy.
  • Prognostics and Health Management (PHM) techniques for predictive maintenance.

Multi-disciplinary Applications:

  • Application of advanced control and fault detection methods in various industries, such as manufacturing, aerospace, automotive, and construction.
  • Hydrotronics systems that integrate hydraulic and electronic components or technologies.
  • Automotive active hydraulic suspension systems continuously monitor various parameters, including vehicle speed, acceleration, steering angle, and wheel movement, to make real-time adjustments to the suspension.
  • Smart fluid power components and systems.
  • Case studies showcasing successful implementation of these techniques in real-world hydraulic systems.

Integration of emerging technologies, such as Internet of Things (IoT) and Industry 4.0, in enhancing control and fault detection capabilities.

Dr. Piotr Czop
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • fault detection techniques
  • hydraulic machines
  • hydraulic systems
  • model-based control
  • nonlinear control
  • robust control
  • intelligent control
  • fuzzy logic control
  • neural networks
  • genetic algorithms
  • data-driven fault detection
  • sensor fusion
  • prognostics and health management (PHM)
  • predictive maintenance
  • nonlinear dynamics
  • adaptive control
  • sliding mode control
  • backstepping control
  • condition monitoring
  • real-time fault detection

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

22 pages, 6340 KiB  
Article
Detecting Anomalies in Hydraulically Adjusted Servomotors Based on a Multi-Scale One-Dimensional Residual Neural Network and GA-SVDD
by Xukang Yang, Anqi Jiang, Wanlu Jiang, Yonghui Zhao, Enyu Tang and Zhiqian Qi
Machines 2024, 12(9), 599; https://doi.org/10.3390/machines12090599 - 28 Aug 2024
Viewed by 305
Abstract
A high-pressure hydraulically adjusted servomotor is an electromechanical–hydraulic integrated system centered on a servo valve that plays a crucial role in ensuring the safe and stable operation of steam turbines. To address the issues of difficult fault diagnoses and the low maintenance efficiency [...] Read more.
A high-pressure hydraulically adjusted servomotor is an electromechanical–hydraulic integrated system centered on a servo valve that plays a crucial role in ensuring the safe and stable operation of steam turbines. To address the issues of difficult fault diagnoses and the low maintenance efficiency of adjusted hydraulic servomotors, this study proposes a model for detecting abnormalities of hydraulically adjusted servomotors. This model uses a multi-scale one-dimensional residual neural network (M1D_ResNet) for feature extraction and a genetic algorithm (GA)-optimized support vector data description (SVDD). Firstly, the multi-scale features of the vibration signals of the hydraulically adjusted servomotor were extracted and fused using one-dimensional convolutional blocks with three different scales to construct a multi-scale one-dimensional residual neural network binary classification model capable of recognizing normal and abnormal states. Then, this model was used as a feature extractor to create a feature set of normal data. Finally, an abnormal detection model for the hydraulically adjusted servomotor was constructed by optimizing the support vector data domain based on this feature set using a genetic algorithm. The proposed method was experimentally validated on a hydraulically adjusted servomotor dataset. The results showed that, compared with the traditional single-scale one-dimensional residual neural network, the multi-scale feature vectors fused by the multi-scale one-dimensional convolutional neural network contained richer state-sensitive information, effectively improving the performance of detecting abnormalities in the hydraulically adjusted servomotor. Full article
(This article belongs to the Special Issue Advanced Control and Fault Detection Techniques in Hydraulic Machines and Systems)
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23 pages, 5925 KiB  
Article
Output Feedback-Based Neural Network Sliding Mode Control for Electro-Hydrostatic Systems with Unknown Uncertainties
by Tri Dung Dang, Tri Cuong Do and Hoai Vu Anh Truong
Machines 2024, 12(8), 554; https://doi.org/10.3390/machines12080554 - 13 Aug 2024
Viewed by 434
Abstract
This paper proposes an output feedback-based control for uncertain electro-hydrostatic systems (EHSs) to satisfy high output tracking precision under the influences of unknown mismatched and matched uncertainties and unstructured dynamical behavior. In this configuration, an extended state observer (ESO) is first employed to [...] Read more.
This paper proposes an output feedback-based control for uncertain electro-hydrostatic systems (EHSs) to satisfy high output tracking precision under the influences of unknown mismatched and matched uncertainties and unstructured dynamical behavior. In this configuration, an extended state observer (ESO) is first employed to obtain unmeasured states and suppress the adverse effect of matched uncertainty. Meanwhile, the influence of unstructured dynamical behavior is approximated by employing a radial basis function neural network (RBFNN)-based technique. With the unmeasured states observed, matched uncertainty, and system dynamics compensated, the robust backstepping sliding mode control is accordingly established and the lumped mismatched uncertainty is then suppressed through disturbance observer-based adaptive law. Interestingly, the proposed control methodology requires only output feedback but can address the whole system dynamics. The stability of the closed-loop system is theoretically proven through a Lyapunov theorem and the effectiveness of the proposed methodology is demonstrated through comparative simulations. Full article
(This article belongs to the Special Issue Advanced Control and Fault Detection Techniques in Hydraulic Machines and Systems)
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18 pages, 3726 KiB  
Article
Modeling of Filtration Phenomenon in Hydrostatic Drives
by Klaudiusz Klarecki, Dominik Rabsztyn and Piotr Czop
Machines 2024, 12(6), 417; https://doi.org/10.3390/machines12060417 - 18 Jun 2024
Cited by 1 | Viewed by 686
Abstract
Some users consider modern hydrostatic drives and controls to be unreliable and difficult to maintain. This view is often due to operational problems caused by issues with obtaining and then maintaining the appropriate cleanliness class of the working fluid. Recommendations on the selection [...] Read more.
Some users consider modern hydrostatic drives and controls to be unreliable and difficult to maintain. This view is often due to operational problems caused by issues with obtaining and then maintaining the appropriate cleanliness class of the working fluid. Recommendations on the selection of appropriate filtration system elements can be found in the literature, but there is no numerical model that could be helpful in a detailed analysis of the phenomenon. In the article, the authors tried to fill the research gap regarding the lack of a filtration model based on the filtration efficiency coefficient of filter elements used in hydraulic drives and controls. The developed model allows users to determine the influence of selected filtration system parameters on the separation of contaminants by filter elements. The model is intended to help designers and users of hydraulic drives and controls in optimizing the filtration system in order to obtain and then maintain the required cleanliness class of the hydraulic fluid. This paper also includes the results of the sensitivity analysis of selected filtration-system operating parameters in terms of the highest efficiency. In order to verify the developed model, experimental tests were also carried out, with the results presented in this paper. Based on the numerical analyses and experimental studies, recommendations that may be helpful in the selection or development of filtration systems used in hydrostatic drives and controls were developed. Full article
(This article belongs to the Special Issue Advanced Control and Fault Detection Techniques in Hydraulic Machines and Systems)
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18 pages, 5354 KiB  
Article
Proposed Feedback-Linearized Integral Sliding Mode Control for an Electro-Hydraulic Servo Material Testing Machine
by Chungeng Sun, Jipeng Li, Ying Tan and Zhijie Duan
Machines 2024, 12(3), 164; https://doi.org/10.3390/machines12030164 - 28 Feb 2024
Cited by 1 | Viewed by 1105
Abstract
High-precision tracking of an electro-hydraulic servo material testing machine’s force control system was achieved using a proposed integral sliding mode control method based on feedback linearization to improve the machine’s force control performance and anti-interference ability. First, the electro-hydraulic servo system’s nonlinear mathematical [...] Read more.
High-precision tracking of an electro-hydraulic servo material testing machine’s force control system was achieved using a proposed integral sliding mode control method based on feedback linearization to improve the machine’s force control performance and anti-interference ability. First, the electro-hydraulic servo system’s nonlinear mathematical model was established, and its input–output linearization was realized using differential geometry theory. Second, integral sliding mode control was introduced into the controller and the feedback-linearized integral sliding mode controller was designed. The controller’s stability was proven based on the Lyapunov stability principle. Finally, a simulation model of the electro-hydraulic servo material testing machine’s force control system was established using AMESim/Simulink software. The designed controller was simulated and verified, and the control effects of the system’s different amplitudes and frequency signals were analyzed. The results showed that the feedback-linearized integral sliding mode control algorithm could effectively improve the system’s force tracking accuracy and parameter adaptability, yielding better robustness and a better control effect. Full article
(This article belongs to the Special Issue Advanced Control and Fault Detection Techniques in Hydraulic Machines and Systems)
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