Diagnostic Signal Method for Fault Identification of Electro-Hydraulic Servo Actuators
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Abstract
In this paper, we develop a fault identification approach for electro-hydraulic servo actuators based on injecting a pre-defined diagnostic signal into the system and then extracting fault-related features from the phase space topology. Next, we build regression models using an artificial neural network, which maps the feature space to fault space to identify the faults represented by the system’s parameters. The performance of the proposed fault identification approach is evaluated when the degradation of permanent armature occurs. The effect of parametric faults on the dynamics is studied and discussed. The different excitation of the system is considered, and the robustness of the proposed method under the condition of noise is also explored. The obtained results indicate the effectiveness of injected diagnostic signals in enriching the dynamics of the system and increasing the quality of extracted features and the accuracy of trained artificial neural networks.
How to Cite
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Online diagnostic, Injected diagnostic signal, Artificial neural network, Electro-hydraulic servo system, Phase space topology
Arellano-Padilla, J., Sumner, M., & Gerada, C. (2011). Winding condition monitoring scheme for a permanent magnet machine using high-frequency injection. IET Electric power applications, 5(1), 89–99.
Briz, F., Degner, M. W., Diez, A. B., & Guerrero, J. M. (2004). Online diagnostics in inverter-fed induction machines using high-frequency signal injection. IEEE Transactions on Industry Applications, 40(4), 1153– 1161.
Deng, W., & Yao, J. (2019). Extended-state-observer-based adaptive control of electrohydraulic servomechanisms without velocity measurement. IEEE/ASME Transactions on Mechatronics, 25(3), 1151–1161.
Ersfolk, J., Ahopelto, M., Lund, W., Wiik, J., Walden, M., ´ Linjama, M., & Westerholm, J. (2018). Online fault identification of digital hydraulic valves using a combined model-based and data-driven approach. arXiv preprint arXiv:1803.05644.
Goharrizi, A. Y., & Sepehri, N. (2011). Internal leakage detection in hydraulic actuators using empirical mode decomposition and Hilbert spectrum. IEEE Transactions on Instrumentation and Measurement, 61(2), 368–378.
Gordic, D., Babi ´ c, M., & Jovi ´ ciˇ c, N. (2004). Modeling´ of spool position feedback servovalves. International Journal of Fluid Power, 5(1), 37–51.
Guo, Q., & Chen, Z. (2021). Neural adaptive control of single-rod electrohydraulic system with lumped uncertainty. Mechanical Systems and Signal Processing, 146, 106869.
Huang, K., Wu, S., Li, F., Yang, C., & Gui, W. (2021). Fault diagnosis of hydraulic systems based on deep learning model with multirate data samples. IEEE Transactions on neural networks and learning systems, 33(11), 6789–6801.
Jegadeeshwaran, R., & Sugumaran, V. (2015). Fault diagnosis of automobile hydraulic brake system using statistical features and support vector machines. Mechanical Systems and Signal Processing, 52, 436–446.
Jiang, J., & Holtz, J. (1997). Accurate estimation of rotor position and speed of induction motors near standstill. In Proceedings of second international conference on power electronics and drive systems (Vol. 1, pp. 1–5).
Kim, G.-W., & Wang, K. (2007). On-line monitoring of fluid effective bulk modulus using piezoelectric transducer impedance. In Asme international mechanical engineering congress and exposition (Vol. 43041, pp. 129–136).
Kim, S.-K., & Seok, J.-K. (2011). High-frequency signal injection-based rotor bar fault detection of inverter-fed induction motors with closed rotor slots. IEEE Transactions on Industry Applications, 47(4), 1624–1631.
Lian, R., Xu, Z., & Lu, J. (2013). Online fault diagnosis for hydraulic disc brake system using feature extracted from model and an SVM classifier. In 2013 Chinese automation congress (pp. 228–232).
Mohamad, T. H., Nazari, F., & Nataraj, C. (2020). A review of phase space topology methods for vibration-based fault diagnostics in nonlinear systems. Journal of Vibration Engineering & Technologies, 8, 393–401.
Peng, J., Li, S., & Fan, Y. (2014). Modeling and parameter identification of the vibration characteristics of armature assembly in a torque motor of hydraulic servo valves under electromagnetic excitations. Advances in Mechanical Engineering, 6, 247384.
Qiu, Z., Min, R., Wang, D., & Fan, S. (2022). Energy features fusion based hydraulic cylinder seal wear and internal leakage fault diagnosis method. Measurement, 195, 111042.
Ren, P., Chen, J., Hu, Y., & Yuan, H. (2016). Research on typical wear fault diagnosis of electro-hydraulic servo valve element. In 2016 prognostics and system health management conference (phm-chengdu) (pp. 1–5).
Samadani, M., Kwuimy, C. K., & Nataraj, C. (2015). Modelbased fault diagnostics of nonlinear systems using the features of the phase space response. Communications in Nonlinear Science and Numerical Simulation, 20(2), 583–593.
Soualhi, M., Nguyen, K. T., Medjaher, K., Lebel, D., & Cazaban, D. (2022). Intelligent monitoring of multi-axis robots for online diagnostics of unknown arm deviations. Journal of Intelligent Manufacturing, 1–17.
Tamburrano, P., Plummer, A. R., Distaso, E., & Amirante, R. (2018). A review of electro-hydraulic servovalve research and development. International Journal of Fluid Power, 1–23.
Tang, S., Zhu, Y., & Yuan, S. (2021). An improved convolutional neural network with an adaptable learning rate towards multi-signal fault diagnosis of hydraulic piston pump. Advanced Engineering Informatics, 50, 101406.
Yao, C., Zhao, Z., Chen, Y., Zhao, X., Li, Z., Wang, Y., . . . Wei, G. (2014). Transformer winding deformation diagnostic system using online high frequency signal injection by capacitive coupling. IEEE Transactions on Dielectrics and Electrical Insulation, 21(4), 1486– 1492.
Yao, Z., Yao, J., & Sun, W. (2018). Adaptive rise control of hydraulic systems with multilayer neural networks. IEEE Transactions on Industrial Electronics, 66(11), 8638–864
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