Machine Learning in Fluid Power – Applications, Trends, and Challenges
DOI:
https://doi.org/10.13052/ijfp1439-9776.2724Keywords:
Fluid power, Machine learning, Condition Monitoring, Data Analysis, Predictive ModelingAbstract
The importance of machine learning (ML) in various engineering disciplines has steadily increased over the past several years, primarily due to the rise in computational power and the development of new, powerful algorithms. ML methods have also found significant applications in fluid power technology, offering substantial benefits such as enhanced system performance, optimized design processes, and improved predictive maintenance. These methods are increasingly used to handle complex, nonlinear systems in fluid power, providing advanced solutions for simulation, system design, control strategies, and condition monitoring. In particular, ML techniques can process vast amounts of data to predict system behavior, identify faults, and optimize energy efficiency, leading to more reliable and efficient fluid power systems. This review aims to introduce and discuss the wide range of ML applications and ongoing research in fluid power, offering engineers a comprehensive overview of the available literature. By doing so, we aim to help engineers identify and select the most suitable and promising ML methods to address their specific tasks and challenges.
Downloads
References
T. C. Silva and L. Zhao, Machine Learning in Complex Networks. ProQuest Ebook Central, Cham: Springer International Publishing, 1st ed. 2016 ed., 2016.
R. S. Sutton and A. Barto, Reinforcement learning: An introduction. Adaptive computation and machine learning, Cambridge, Massachusetts and London, England: The MIT Press, second edition ed., 2020.
Y. Li, “Deep reinforcement learning,” Under review for Morgan & Claypool: Synthesis Lectures in Artificial Intelligence and Machine Learning, arXiv, 2018.
M. Raghu and E. Schmidt, “A survey of deep learning for scientific discovery,” 2020-03-26.
A. Géron, Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow. O’Reilly Media, Inc., 2022.
W. S. McCulloch and W. Pitts, “A logical calculus of the ideas immanent in nervous activity,” The Bulletin of Mathematical Biophysics, vol. 5, no. 4, pp. 115–133, 1943.
Stanford Computer Science, “Convolutional neural networks for visual recognition,” https://cs231n.github.io/neural-networks-1/, last visited on 2023-02-22.
K. Hornik, M. Stinchcombe, and H. White, “Multilayer feedforward networks are universal approximators,” Neural Networks, vol. 2, no. 5, pp. 359–366, 1989.
Stanford Edu, “Multi-layer neural network,” http://ufldl.stanford.edu/tutorial/supervised/MultiLayerNeuralNetworks/, last visited on 2023-02-22.
I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Köpf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “Pytorch: An imperative style, high-performance deep learning library,” 2019.
M. Candela-Leal, E. Gutiérrez-Flores, G. Presbítero-Espinosa, A. Sujatha-Ravindran, R. Ramirez-Mendoza, J. Lozoya-Santos, and M. A. Ramírez Moreno, “Multi-output sequential deep learning model for athlete force prediction on a treadmill using 3d markers,” Applied Sciences, vol. 12, p. 5424, 05 2022.
G. James, D. Witten, T. Hastie, and R. Tibshirani, An Introduction to Statistical Learning, vol. 103. New York, NY: Springer New York, 2013.
Stanford Edu, “Pca whitening,” http://ufldl.stanford.edu/tutorial/unsupervised/PCAWhitening/, last visited on 2023-02-22.
Stanford Edu, “Autoencoders,” http://ufldl.stanford.edu/tutorial/unsupervised/Autoencoders/, last visited on 2023-02-22.
D. P. Kingma and M. Welling, “An introduction to variational autoencoders,” Foundations and Trends in Machine Learning, 2019.
David Silver, “Lecture 1: Introduction to reinforcement learning,” https://www.davidsilver.uk/teaching/, last visited on 2023-02-22.
R. Bellman, Dynamic programming. Princeton landmarks in mathematics, Princeton: Princeton University Press, 2010.
L. Wang, K. C. Tan, and D. Liu, “A survey on reinforcement learning for control optimization in engineering systems,” Journal of Control and Decision, vol. 3, no. 1, pp. 19–43, 2016.
W. Su, W. Ren, H. Sun, C. Liu, X. Lu, Y. Hua, H. Wei, and H. Jia, “Data-based flow rate prediction models for independent metering hydraulic valve,” Energies, vol. 15, no. 20, p. 7699, 2022.
A. Widodo and B.-S. Yang, “Support vector machine in machine condition monitoring and fault diagnosis,” Mechanical systems and signal processing, vol. 21, no. 6, pp. 2560–2574, 2007.
M. Xia, T. Li, L. Xu, L. Liu, and C. W. De Silva, “Fault diagnosis for rotating machinery using multiple sensors and convolutional neural networks,” IEEE/ASME transactions on mechatronics, vol. 23, no. 1, pp. 101–110, 2017.
E. Jove, J.-L. Casteleiro-Roca, H. Quintián, J. A. Méndez-Pérez, and J. L. Calvo-Rolle, “A fault detection system based on unsupervised techniques for industrial control loops,” Expert Systems, vol. 36, no. 4, p. e12395, 2019.
N. Thuerey, K. Weißenow, L. Prantl, and X. Hu, “Deep learning methods for reynolds-averaged navier–stokes simulations of airfoil flows,” AIAA journal, vol. 58, no. 1, pp. 25–36, 2020.
J. Rabault and A. Kuhnle, “Accelerating deep reinforcement learning strategies of flow control through a multi-environment approach,” Physics of Fluids, vol. 31, p. 094105, 09 2019.
N. Thuerey, K. Weißenow, L. Prantl, and X. Hu, “Deep learning methods for reynolds-averaged navier–stokes simulations of airfoil flows,” AIAA Journal, vol. 58, p. 25–36, Jan. 2020.
R. Zhao, R. Yan, Z. Chen, K. Mao, P. Wang, and R. X. Gao, “Deep learning and its applications to machine health monitoring: A survey,” 2016.
A. Malhi and R. Gao, “Pca-based feature selection scheme for machine defect classification,” Instrumentation and Measurement, IEEE Transactions on, vol. 53, pp. 1517–1525, 01 2005.
B. Song and A. J. Koivo, “Neural adaptive control of excavators,” in Proceedings 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human Robot Interaction and Cooperative Robots, pp. 162–167, IEEE Comput. Soc. Press, 1995.
Z. Hong, D. Kaifang, and L. Tingqi, “A online-trained neural network controller for electro-hydraulic servo system,” in Proceedings of the 4th World Congress on Intelligent Control and Automation (Cat. No.02EX527), pp. 2983–2986, IEEE, 2002.
I. Kurinov, G. Orzechowski, P. Hamalainen, and A. Mikkola, “Automated excavator based on reinforcement learning and multibody system dynamics,” IEEE Access, vol. 8, pp. 213998–214006, 2020.
S. Backman, D. Lindmark, K. Bodin, M. Servin, J. Mörk, and H. Löfgren, “Continuous control of an underground loader using deep reinforcement learning,” Machines, vol. 9, no. 10, p. 216, 2021.
J. Andersson, K. Bodin, D. Lindmark, M. Servin, and E. Wallin, “Reinforcement learning control of a forestry crane manipulator,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021.
B. Kazenwadel and M. Geimer, “Real-time prediction of efficient operating points in quasi-stationary agricultural processes with hydraulic implements,” 11 2023.
B. J. Hodel, “Learning to operate an excavator via policy optimization,” Procedia Computer Science, vol. 140, pp. 376–382, 2018.
J. Zhang, Q. Wang, and T. Wang, “A two-stage optimal controller for hydraulic loading system with mechanical compensation,” in 2019 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM), pp. 304–309, IEEE, 2019.
F. Brumand-Poor, G. Matthiesen, and K. Schmitz, “Control of a hydromechanical pendulum with a reinforcement learning agent,” in 13. Internationales Fluidtechnisches Kolloquium, IFK, Aachen, 2022.
F. Brumand-Poor, L. Kauderer, G. Matthiesen, and K. Schmitz, “Application of deep reinforcement learning control of an inverted hydraulic pendulum,” International Journal of Fluid Power, 2023.
C. Lizalde, A. Loukianov, and E. Sanchez, “Force tracking neural control for an electro-hydraulic actuator via second order sliding mode,” in Proceedings of the 2005 IEEE International Symposium on, Mediterrean Conference on Control and Automation Intelligent Control, 2005, pp. 292–297, IEEE, 2005.
Y.-g. Peng, J. Wang, and W. Wei, “Model predictive control of servo motor driven constant pump hydraulic system in injection molding process based on neurodynamic optimization,” Journal of Zhejiang University SCIENCE C, vol. 15, no. 2, pp. 139–146, 2014.
A. A. Aly, “Flow rate control of variable displacement piston pump with pressure compensation using neural network,” JES. Journal of Engineering Sciences, vol. 35, no. 6, pp. 1401–1412, 2007.
T. Reidl, P. Schraft, J. Weber, and S. Ihlenfeldt, “Loss optimal control strategy of speed and displacement variable electrohydrostatic axes,” 04 2021.
B. Kazenwadel, S. Becker, M. Graf, and M. Geimer, “Aligning process quality and efficiency in agricultural soil tillage,” at – Automatisierungstechnik, vol. 71, pp. 979–986, 11 2023.
M. Wydra, A. Bauer, G. Chris, and M. Geimer, “Moderne steueralgorithmen für forstkräne mittels künstlichen neuronalen netzen imitieren und optimieren/imitate and optimize modern control algorithms for forestry cranes by means of artificial neural networks,” Landtechnik, vol. 75, pp. 118–140, 06 2020.
H. Chen, “Research of the electro-hydraulic servo system based on rbf fuzzy neural network controller,” Journal of Software, vol. 7, no. 9, 2012.
R. Dhakate, C. Brommer, C. Bohm, H. Gietler, S. Weiss, and J. Steinbrener, “Autonomous control of redundant hydraulic manipulator using reinforcement learning with action feedback,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 7036–7043, IEEE, 2022.
L. Brinkschulte, M. Graf, and M. Geimer, “Reinforcement learning: A control approach for reducing component damage in mobile machines,” 10 2020.
W. Yang, N. Strokina, J. Pajarinen, J. Vihonen, M. M. Aref, and J.-K. Kämäräinen, “Neural network controller for autonomous pile loading revised,” IEEE International Conference on Robotics and Automation (ICRA), Xi’an, China,, pp. 2198–2204, 2021.
E. Halbach, J. Kamarainen, and R. Ghabcheloo, “Neural network pile loading controller trained by demonstration,” in 2019 International Conference on Robotics and Automation (ICRA), pp. 980–986, IEEE, 2019.
P. Egli and M. Hutter, “A general approach for the automation of hydraulic excavator arms using reinforcement learning,” IEEE Robotics and Automation Letters, pp. 5679–5686, vol. 7, no. 2, 2022.
Q. Zhu and Q.-f. Wang, “Real-time energy management controller design for a hybrid excavator using reinforcement learning,” Journal of Zhejiang University-SCIENCE A, vol. 18, no. 11, pp. 855–870, 2017.
C. Pohlandt and M. Geimer, “Self-learning control strategy for electrified off-highway machines to optimize energy efficiency,” SAE International Journal of Commercial Vehicles, vol. 8, pp. 513–518, 09 2015.
M. Karpenko, J. Anderson, and N. Sepehri, “Coordination of hydraulic manipulators by reinforcement learning,” in 2006 American Control Conference, p. 6 pp, IEEE, 2006.
M. Karpenko, N. Sepehri, and J. Anderson, “Decentralized coordinated motion control of two hydraulic actuators handling a common object,” Journal of Dynamic Systems, Measurement, and Control, vol. 129, no. 5, pp. 729–741, 2007.
K. Guo, M. Li, W. Shi, and Y. Pan, “Adaptive tracking control of hydraulic systems with improved parameter convergence,” IEEE Transactions on Industrial Electronics, vol. 69, no. 7, pp. 7140–7150, 2022.
S.-W. Kim, B. Cho, S. Shin, J.-H. Oh, J. Hwangbo, and H.-W. Park, “Force control of a hydraulic actuator with a neural network inverse model,” IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 2814–2821, 2021.
F. Kreutmayr and M. Imlauer, “Application of machine learning to improve to performance of a pressure-controlled system,” in Volume 1 – Symposium, pp. 77–86, Technische Universität Dresden, 12.–14.10.2020.
D. Wyrwal, T. Lindner, P. Nowak, and M. Bialek, “Control strategy of hydraulic cylinder based on deep reinforcement learning,” in 2020 International Conference Mechatronic Systems and Materials (MSM), pp. 1–5, IEEE, 2020.
D. T. Liem, D. Q. Truong, H. G. Park, and K. K. Ahn, “A feedforward neural network fuzzy grey predictor-based controller for force control of an electro-hydraulic actuator,” International Journal of Precision Engineering and Manufacturing, vol. 17, no. 3, pp. 309–321, 2016.
T. Shan, L. Li, X. Wu, and S. Cheng, “Pressure control based on reinforcement learning strategy of the pneumatic relays for an electric-pneumatic braking system,” Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, p. 095440702211088, 2022.
F. Mesmer, T. Szabo, and K. Graichen, “Learning feedforward control of a hydraulic clutch actuation path based on policy gradients,” in 2020 59th IEEE Conference on Decision and Control (CDC), pp. 585–590, IEEE, 2020.
T. Bécsi, S. Aradi, Á. Szabó, and P. Gáspár, “Policy gradient based reinforcement learning control design of an electro-pneumatic gearbox actuator,” IFAC-PapersOnLine, vol. 51, no. 22, pp. 405–411, 2018.
T. Becsi, A. Szabo, B. Kovari, S. Aradi, and P. Gaspar, “Reinforcement learning based control design for a floating piston pneumatic gearbox actuator,” IEEE Access, vol. 8, pp. 147295–147312, 2020.
X.-x. Yin, Y.-g. Lin, and W. Li, “Predictive pitch control of an electro-hydraulic digital pitch system for wind turbines based on the extreme learning machine,” Transactions of the Institute of Measurement and Control, vol. 38, no. 11, pp. 1392–1400, 2016.
F. Theljani, K. Laabidi, M. Lahmari-Ksouri, and S. Zidi, “New approach for systems monitoring based on semi-supervised classification,” in 2011 International Conference on Communications, Computing and Control Applications (CCCA), pp. 1–6, IEEE, 2011.
V. Vígolo, V. Boyko, J. Weber, A. Valdiero, and V. De Negri, “Online monitoring of pneumatic actuation system for energy efficiency and dynamic performance,” 11 2023.
E. Emad, J. Weber, and M. Wahler, “A machine learning approach for tracking the torque losses in internal gear pump – ac motor units,” in 10th International Fluid Power Conference (10. IFK), 2016.
H. M. Castilho, C. L. Nascimento, and W. O. L. Vianna, “Aircraft bleed valve fault classification using support vector machines and classification trees,” in 2018 Annual IEEE International Systems Conference (SysCon), pp. 1–7, IEEE, 2018.
V. Vígolo, V. Boyko, J. Weber, and V. De Negri, “A model-based approach for online optimization of pneumatic drives,” IEEE/ASME Transactions on Mechatronics, vol. PP, pp. 1–11, 01 2024.
H. Sano, J. P. P. Malere, and L. Berton, “Single and multiple failures diagnostics of pneumatic valves using machine learning,” Conference Encontro Nacional de Inteligência Artificial e Computacional, Brasil, 2019.
P. Borriello, A. Pawar, E. Frosina, F. Tessicini, A. Vacca, and A. Senatore, “Detection of typical manufacturing errors in external gear machines using numerical simulation and data driven machine learning,” 11 2023.
A. M. Gareev, E. Y. Minaev, D. M. Stadnik, N. S. Davydov, V. I. Protsenko, I. A. Popelniuk, A. V. Nikonorov, and A. G. Gimadiev, “Machine-learning algorithms for helicopter hydraulic faults detection: model based research,” Journal of Physics: Conference Series, vol. 1368, no. 5, p. 052027, 2019.
V. Singh, A. Abdul Azeez, and T. Minav, “Intelligent approach to enhance redundancy in novel steer-by-wire for heavy earth moving machinery,” 06 2024.
F. Kosova and H. O. Unver, “A digital twin framework for aircraft hydraulic systems failure detection using machine learning techniques,” Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, p. 095440622211326, 2022.
V. Zakharov, A. Abdul Azeez, X. Han, and T. Minav, “The impact of electric drive structures on sensorless ai-based hydraulic valve fault classification,” 11 2023.
N. Keller, A. Sciancalepore, and A. Vacca, “Condition monitoring of an axial piston pump on a mini excavator,” International Journal of Fluid Power, 05 2023.
N. Keller, A. Sciancalepore, and A. Vacca, “Demonstrating a condition monitoring process for axial piston pumps with damaged valve plates,” International Journal of Fluid Power, 02 2022.
K. Shen and D. Zhao, “Fault diagnosis for aircraft hydraulic systems via one-dimensional multichannel convolution neural network,” Actuators, vol. 11, no. 7, p. 182, 2022.
A. Abdul Azeez, E. Vuorinen, T. Minav, and P. Casoli, “Ai-based condition monitoring of a variable displacement axial piston pump,” 06 2022.
P. Borriello, A. Senatore, F. Tessicini, G. Ricucci, and E. Frosina, “A fault detection strategy for an epump during eol tests based on a knowledge-based vibroacoustic tool and supervised machine learning classifiers,” Meccanica, vol. 59, pp. 1–26, 02 2024.
Y. Zhang, A. Vacca, G. Gong, and H. Yang, “Quantitative fault diagnostics of hydraulic cylinder using particle filter,” Machines, vol. 11, no. 11, p. 1019, 2023.
T. T. Le, J. Watton, and D. T. Pham, “An artificial neural network based approach to fault diagnosis and classification of fluid power systems,” Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, vol. 211, no. 4, pp. 307–317, 1997.
J. Huang, X. Wang, D. Wang, Z. Wang, and X. Hua, “Analysis of weak fault in hydraulic system based on multi-scale permutation entropy of fault-sensitive intrinsic mode function and deep belief network,” Entropy (Basel, Switzerland), vol. 21, no. 4, 2019.
F. Makansi and K. Schmitz, “Simulation-based data sampling for condition monitoring of fluid power drives,” IOP Conference Series: Materials Science and Engineering, vol. 1097, no. 1, p. 012018, 2021.
F. Makansi and K. Schmitz, “Data-driven condition monitoring of a hydraulic press using supervised learning and neural networks,” Energies, vol. 15, no. 17, p. 6217, 2022.
F. Makansi and K. Schmitz, “Fault detection and diagnosis for a hydraulic press by use of a mixed domain database,” in BATH/ASME 2022 Symposium on Fluid Power and Motion Control, American Society of Mechanical Engineers, 2022.
A. El-Betar, M. Abdelhamed, A. El-Assal, and R. Abdelsatar, “Fault diagnosis of a hydraulic power system using an artificial neural network,” Journal of King Abdulaziz University-Engineering Sciences, vol. 17, no. 1, pp. 115–136, 2006.
N. Helwig, E. Pignanelli, and A. Schutze, “Condition monitoring of a complex hydraulic system using multivariate statistics,” in 2015 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings, pp. 210–215, IEEE, 2015.
A. Gareev, V. Protsenko, D. Stadnik, P. Greshniakov, Y. Yuzifovich, E. Minaev, A. Gimadiev, and A. Nikonorov, “Improved fault diagnosis in hydraulic systems with gated convolutional autoencoder and partially simulated data,” Sensors (Basel, Switzerland), vol. 21, no. 13, 2021.
A. M. Gareev, E. V. Shakhmatov, A. B. Prokofev, and D. M. Stadnik, “Machine learning method for predicting remaining useful life of hydraulic equipment,” Journal of Machinery Manufacture and Reliability, vol. 51, no. 3, pp. 253–260, 2022.
A. Mallak and M. Fathi, “Sensor and component fault detection and diagnosis for hydraulic machinery integrating lstm autoencoder detector and diagnostic classifiers,” Sensors (Basel, Switzerland), vol. 21, no. 2, 2021.
Z. Huijie, R. Ting, W. Xinqing, Z. You, and F. Husheng, “Fault diagnosis of hydraulic pump based on stacked autoencoders,” in 2015 12th IEEE International Conference on Electronic Measurement & Instruments (ICEMI), pp. 58–62, IEEE, 2015.
X. Tao, Z. Wang, J. Ma, and H. Fan, “Study on fault detection using wavelet packet and som neural network,” in Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing), pp. 1–5, IEEE, 2012.
J. Bei, C. Lu, and Z. Wang, “Performance assessment and fault diagnosis for hydraulic pump based on wpt and som,” Vibroengineering Procedia, vol. 2, pp. 23–28, 01 2013.
S. Tang, S. Yuan, Y. Zhu, and G. Li, “An integrated deep learning method towards fault diagnosis of hydraulic axial piston pump,” Sensors (Basel, Switzerland), vol. 20, no. 22, 2020.
S. Tang, Y. Zhu, S. Yuan, and G. Li, “Intelligent diagnosis towards hydraulic axial piston pump using a novel integrated cnn model,” Sensors (Basel, Switzerland), vol. 20, no. 24, 2020.
Y. Zhu, G. Li, R. Wang, S. Tang, H. Su, and K. Cao, “Intelligent fault diagnosis of hydraulic piston pump based on wavelet analysis and improved alexnet,” Sensors (Basel, Switzerland), vol. 21, no. 2, 2021.
C. Lu, N. Ma, and Z. Wang, “Fault detection for hydraulic pump based on chaotic parallel rbf network,” EURASIP Journal on Advances in Signal Processing, vol. 2011, no. 1, 2011.
S. Rajakarunakaran, D. Devaraj, and K. S. Rao, “Fault detection in centrifugal pumping systems using neural networks,” International Journal of Modelling, Identification and Control, vol. 3, no. 2, p. 131, 2008.
M. Karpenko and N. Sepehri, “A neural network based fault detection and identification scheme for pneumatic process control valves,” in E-systems and e-man for cybernetics in cyberspace, (Piscataway, NJ), pp. 93–98, IEEE Service Center, 2001.
M. Karpenko, N. Sepehri, and D. Scuse, “Diagnosis of process valve actuator faults using a multilayer neural network,” Control Engineering Practice, vol. 11, no. 11, pp. 1289–1299, 2003.
Y. Duensing, A. Rodas Rivas, and K. Schmitz, “Machine learning for failure mode detection in mobile machinery,” Karlsruhe : KIT Scientific Publishing, Kolloquium Mobilhydraulik, Karlsruhe, vol. 11, pp. 1–26, 2020.
Y. Joo, K. Kim, and J. Jeong, “Performance comparison of machine learning algorithms for imbalanced class classification in hydraulic system,” in 2020 14th International Conference on Ubiquitous Information Management and Communication (IMCOM), pp. 1–8, IEEE, 2020.
N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “Smote: Synthetic minority over-sampling technique,” Journal of Artificial Intelligence Research, vol. 16, pp. 321–357, 2002.
H. Han, W.-Y. Wang, and B.-H. Mao, “Borderline-smote: A new over-sampling method in imbalanced data sets learning,” in Advances in intelligent computing (D. Hutchison, T. Kanade, J. Kittler, J. M. Kleinberg, F. Mattern, J. C. Mitchell, M. Naor, O. Nierstrasz, C. Pandu Rangan, B. Steffen, M. Sudan, D. Terzopoulos, D. Tygar, M. Y. Vardi, G. Weikum, D.-S. Huang, X.-P. Zhang, and G.-B. Huang, eds.), vol. 3644 of Lecture Notes in Computer Science, pp. 878–887, Berlin: Springer, 2005.
H. He, Y. Bai, E. A. Garcia, and S. Li, “Adasyn: Adaptive synthetic sampling approach for imbalanced learning,” in 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), pp. 1322–1328, IEEE, 2008.
I. Tomek, “Two modifications of cnn,” IEEE Transactions on Systems, Man, and Cybernetics, vol. SMC-6, no. 11, pp. 769–772, 1976.
L. Breiman, “Bagging predictors,” Machine Learning, vol. 24, no. 2, pp. 123–140, 1996.
L. Breiman, “Random forest,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001.
P. Geurts, D. Ernst, and L. Wehenkel, “Extremely randomized trees,” Machine Learning, vol. 63, no. 1, pp. 3–42, 2006.
Y. Freund and R. E. Schapire, “A decision-theoretic generalization of on-line learning and an application to boosting,” Journal of Computer and System Sciences, vol. 55, no. 1, pp. 119–139, 1997.
J. H. Friedman, “Greedy function approximation: A gradient boosting machine,” The Annals of Statistics, vol. 29, no. 5, 2001.
J. Liu, A. Sitte, and J. Weber, “Adaptive identification and application of flow mapping for electrohydraulic valves,” 06 2021.
F. Brumand-Poor, F. Makansi, L. Jiakun, and K. Schmitz, “Implementation of a variational autoencoder for dimension reduction of a hydraulic system.” Global Fluid Power Symposium , Neapel, Global Fluid Power Symposium, Neapel, 2022.
Y. Xiang, T. Tang, T. Su, C. Brach, L. Liu, S. Mao, and M. Geimer, “Fast crdnn: Towards on site training of mobile construction machines,” IEEE Access, vol. PP, pp. 1–1, 09 2021.
W. Su, W. Ren, H. Sun, C. Liu, X. Lu, Y. Hua, H. Wei, and H. Jia, “Data-based flow rate prediction models for independent metering hydraulic valve,” Energies, vol. 15, no. 20, p. 7699, 2022.
S. Zhang, T. Chen, T. Minav, X. Cao, and A. Wu, “Position soft-sensing of direct-driven hydraulic system based on back propagation neural network,” Actuators, vol. 10, p. 18, 12 2021.
L. Baronti, B. Zhang, M. Castellani, and D. T. Pham, “Machine learning of electro-hydraulic motor dynamics,” SN Applied Sciences, vol. 2, no. 1, 2020.
B. Helian, X. An, Z. Yong, Z. Chen, and M. Geimer, “Lstm-based workload recognition for hydraulic actuators: A case study on excavator digging process,” 10 2024.
K. O. Achieng, “Evaluating pump performance using laboratory observations and machine learning,” ISH Journal of Hydraulic Engineering, vol. 27, no. sup1, pp. 174–181, 2021.
G. Chris, M. Geimer, and N. Maier, “Assistance system for an automated log-quality and assortment estimation based on data-driven approaches using hydraulic signals of forestry machines,” 06 2020.
H. Hosseiny, F. Nazari, V. Smith, and C. Nataraj, “A framework for modeling flood depth using a hybrid of hydraulics and machine learning,” Scientific reports, vol. 10, no. 1, p. 8222, 2020.
B. Helian, X. Huang, M. Yang, Y. Bian, and M. Geimer, “Computer vision-based excavator bucket fill estimation using depth map and faster r-cnn,” Automation in Construction, vol. 166, p. 105592, 07 2024.
L. Boden, M. Hofmeister, F. Brumand-Poor, L. Pleninger, and K. Schmitz, “Predicting compatibility of sealing material with bio hybrid fuels development and comparison of machine learning methods,” 09 2024.
L. Boden, M. Hofmeister, F. Brumand-Poor, L. Pleninger, and K. Schmitz, “Predicting compatibility of sealing material with bio hybrid fuels development and comparison of machine learning methods,” 09 2024.
F. Brumand-Poor, N. Bauer, N. Plückhahn, and K. Schmitz, “Fast computation of lubricated contacts: A physics-informed deep learning approach,” International Journal of Fluid Power, vol. 19, pp. 1–12, 2024.
F. Brumand-Poor, N. Bauer, N. Plückhahn, M. Thebelt, S. Woyda, and K. Schmitz, “Extrapolation of hydrodynamic pressure in lubricated contacts: A novel multi-case physics-informed neural network framework,” Lubricants, vol. 12, no. 4, p. 122, 2024.
F. Brumand-Poor, M. Rom, N. Plückhahn, and K. Schmitz, “Physics-informed deep learning for lubricated contacts with surface roughness as parameter.” 63. Tribologie-Fachtagung 2022 , Göttingen , Germany, 2024.
F. Brumand-Poor, F. Barlog, N. Plückhahn, M. Thebelt, and K. Schmitz, “Advancing lubrication calculation: A physics-informed neural network framework for transient effects and cavitation phenomena in reciprocating seals.” 22nd International Sealing Conference, Stuttgart, Germany, 2024.
F. Brumand-Poor, F. Barlog, N. Plückhahn, M. Thebelt, N. Bauer, and K. Schmitz, “Physics-informed neural networks for the reynolds equation with transient cavitation modeling,” Preprints, October 2024.
A. Al-Issa and J. Weber, “Predicting hydraulic oil thermophysical properties using physics-informed neural networks,” International Journal of Fluid Power, 2024.
A. Al-Issa and J. Weber, “Predicting hydraulic oil thermophysical properties using physics-informed neural networks,” International Journal of Fluid Power, vol. 25, pp. 59–88, 07 2024.
A. Al-Issa, T. Schulze, and J. Weber, “Rapid thermal energy modeling and analysis of complex industrial hydraulic systems,” International Journal of Fluid Power, vol. 26, pp. 65–98, 04 2025.
L. Boden, F. Brumand-Poor, L. Pleninger, and K. Schmitz, “Enhancing predictive accuracy under data scarcity: Modeling molecular interactions to describe sealing material compatibility with bio-hybrid fuels,” Physchem, vol. 5, 04 2025.
