Fault detection in axial piston hydraulic pumps: integrating principal component analysis with silhouette-based cluster evaluation
DOI:
https://doi.org/10.29019/enfoqueute.1120Keywords:
Principal Component Analysis, Silhouette Analysis, Failure Detection, hydraulic piston pumpAbstract
This paper presents an approach integrating principal component and silhouette analysis with clustering algorithms for fault detection in hydraulic systems. The methodology was validated through a study in which vibration and pressure signals were collected under normal and fault conditions. These signals were then processed through filtering and normalization, followed by dimensionality reduction using principal component analysis. The resulting lower-dimensional feature vectors retained the critical characteristics of both normal and faulty conditions and were subsequently fed into a clustering algorithm. The quality of the resulting clusters was evaluated using silhouette analysis, which offers a reliable means of assessing cluster quality and visualising the outcomes of fault classification. The study demonstrates the effectiveness of this method in accurately representing the patterns of normal and malfunctioning hydraulic pump conditions, ultimately leading to successful diagnostic results.
Downloads
References
[1] S. Liu et al., A new test method for simulating wear failure of hydraulic pump slipper pair under high-speed and high-pressure conditions, Front. Energy Res., vol. 10, no. January, pp. 1-14, 2023, https://doi.org/10.3389/fenrg.2022.1096633
[2] M. T. Amin, F. Khan, S. Imtiaz and S. Ahmed, Robust Process Monitoring Methodology for Detection and Diagnosis of Unobservable Faults, Ind. Eng. Chem. Res., vol. 58, no. 41, pp. 19149-19165, 2019. https://doi.org/10.1021/acs.iecr.9b03406
[3] G. Patterson-hine, G. Aaseng, S. Biswas, S.Narasimhan and K. Pattipati, “A Review of Diagnostic Techniques for ISHM Applications”, NASA Ames Res. Cent. Honeywell Def. Sp. Electron. Syst., vol. 1, 2005.
[4] J. Watton, Modelling, Monitoring and Diagnostic Techniques for Fluid Power Systems. Wales: Springer, 2007.
[5] V. K. Kandula, Fault detection in process control plants using principal component analysis (LSU Master’s Theses). 2011.
[6] I. T. Jolliffe, “Principal Component Analysis, Second Edition”, Encycl. Stat. Behav. Sci., vol. 30, no. 3, pp. 487, 2002. https://doi.org/10.2307/1270093
[7] J. Mina and C. Verde, Detección de fallas usando análisis de componentes principales, pp. 431-436, 2004. Instituto de Ingeniería, UNAM.
[8] L. Siyuan et al., “Application of PCA for fault detection in Hydraulic Pumps,” Journal of Hydraulic Engineering, vol. 15, no. 4, pp. 234-245, 2010.
[9] L. Siyuan et al., “Rough Set and PCA for fault diagnosis in hydraulic systems”, International Journal of Fluid Power, vol. 10, no. 2, pp. 50-160, 2012.
[10] M. A. Atoui et al., “Bayesian Networks and PCA for fault detection in Tennessee Eastman Process,” AI in Manufacturing, vol. 5, no. 1, pp. 45-58, 2015.
[11] Villegas et al., “PCA application for fault detection in real plants”, IEEE Transactions on Industrial Applications, vol. 29, pp. 255-263, 2017.
[12] A. Hichri, M. Hajji, M. Mansouri, H. Nounou and K. Bouzrara, “Supervised machine learning-based salp swarm algorithm for fault diagnosis of photovoltaic systems”, J. Eng. Appl. Sci., vol. 71, no. 1, pp. 1-17, 2024, https://doi.org/10.1186/s44147-023-00344-z
[13] X. Ying Huang, H. Xia, W. zhe Yin and Y. kuo Liu, “Research on fault diagnosis and fault location of nuclear power plant equipment”, Ann. Nucl. Energy, vol. 205, no. April, p. 110556, 2024, https://doi.org/10.1016/j.anucene.2024.110556
[14] H. Zhao et al., “PCA and Salp Swarm Algorithm for fault detection in photovoltaic systems”, Renewable Energy, vol. 23, pp. 98-107, 2019.
[15] Y. Cardenas, G. Carrillo, A. Alviz, A. Alviz, I. Portnoy, J. Fajardo, E. Ocampo and E. Da-Costa, “Application of a Pca-Based Fault Detection and Diagnosis Method in a Power Generation System With a 2 Mw Natural Gas Engine”, EUREKA, Phys. Eng., vol. 2022, no. 6, pp. 84-98, 2022, https://doi.org/10.21303/2461-4262.2022.002701
[16] B. Xu, X. Huang, J. Zhang, W. Huang, F. Lyu and H. Xu, “A Fault Detection Method for a Practical Electro-Hydraulic Variable-Displacement Pump with Unknown Swashplate Moment”, IEEE Trans. Instrum. Meas., vol. 72, pp. 1-11, 2023, https://doi.org/10.1109/TIM.2023.3265090
[17] J. G. Maradey Lázaro and C. Borrás Pinilla, “A methodology for detection of wear in hydraulic axial piston pumps”, Int. J. Interact. Des. Manuf., vol. 14, no. 3, pp. 1103-1119, 2020, https://doi.org/10.1007/s12008-020-00681-w
[18] H. Yu, H. Li, and Y. Li, “Vibration signal fusion using improved empirical wavelet transform and variance contribution rate for weak fault detection of hydraulic pumps”, ISA Trans., vol. 107, pp. 385-401, 2020, https://doi.org/10.1016/j.isatra.2020.07.025
[19] A. Dabrowska, R. Stetter, H. Sasmito and S. Kleinmann, “Extended Kalman Filter algorithm for advanced diagnosis of positive displacement pumps”, IFAC Proceedings, vol. 45, no. 20, https://doi.org/10.3182/20120829-3-MX-2028.00068
[20] J. Konieczny and J. Stojek, “Use of the k-nearest neighbour classifier in wear condition classification of a positive displacement pump”, Sensors, vol. 21, no. 18, 2021, https://doi.org/10.3390/s21186247
[21] J. M. Liu, L. C. Gu, and B. L. Geng, “A practical signal processing approach for fault detection of axial piston pumps using instantaneous angular speed”, Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci., vol. 234, no. 19, pp. 3935-3947, 2020, https://doi.org/10.1177/0954406220917704
[22] J. Konieczny, W. Łatas, and J. Stojek, “Classification of Wear State for a Positive Displacement Pump Using Deep Machine Learning †”, Energies, vol. 16, no. 3, pp. 1-19, 2023, https://doi.org/10.3390/en16031408
[23] R. A. Galo Fernandes, P. M. Silva Rocha Rizol, A. Nascimento, and J. A. Matelli, “A Fuzzy Inference System for Detection of Positive Displacement Motor (PDM) Stalls during Coiled Tubing Operations”, Appl. Sci., vol. 12, no. 19, 2022, https://doi.org/10.3390/app12199883
[24] C. Liu, J. Bai, and F. Wu, “Fault Diagnosis Using Dynamic Principal Component Analysis and GA Feature Selection Modeling for Industrial Processes”, Processes, vol. 10, no. 12, 2022, https://doi.org/10.3390/pr10122570
[25] Y. Huo, Y. Cao, Z. Wang, Y. Yan, Z. Ge, and Y. Yang, “Traffic anomaly detection method based on improved GRU and EFMS-Kmeans clustering”, Comput. Model. Eng. Sci., vol. 126, no. 3, pp. 1053-1091, 2021, https://doi.org/10.32604/cmes.2021.013045
[26] J. Wu, Advances in K-means Clustering a Data Mining Thinking. Springer-Verlag Berlin Heidelberg, 2012.
[27] C. Dong, J. Tao, H. Sun, Q. Wei, H. Tan, and C. Liu, “Innovative fault diagnosis for axial piston pumps: A physics-informed neural network framework predicting pump flow ripple”, Mech. Syst. Signal Process., vol. 225, no. January, p. 112274, 2025, https://doi.org/10.1016/j.ymssp.2024.112274
[28] Z. Dong, H. An, S. Liu, S. Ma, Y. G, H. Pan and Ch. Ai, “Domain adversarial transfer fault diagnosis method of an axial piston pump based on a multi-scale attention mechanism”, Meas. J. Int. Meas. Confed., vol. 239, no. July 2024, p. 115455, 2025, https://doi.org/10.1016/j.measurement.2024.115455
[29] D. Ling, T. Jiang, Y. Zheng and Y. Wang, “An adaptive mode identification and fault detection scheme for nonlinear multimode process monitoring using improved DPC-KPCA”, J. Taiwan Inst. Chem. Eng}., vol. 169, no. January, p. 105915, 2025, https://doi.org/10.1016/j.jtice.2024.105915
Published
Issue
Section
License
Copyright (c) 2025 The Authors
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The authors retain all copyrights ©.
- The authors retain their trademark and patent rights, as well as rights to any process or procedure described in the article.
- The authors retain the right to share, copy, distribute, perform, and publicly communicate the article published in Enfoque UTE (for example, post it in an institutional repository or publish it in a book), provided that acknowledgment of its initial publication in Enfoque UTE is given.
- The authors retain the right to publish their work at a later date, to use the article or any part of it (for example, a compilation of their work, lecture notes, a thesis, or for a book), provided that they indicate the source of publication (authors of the work, journal, volume, issue, and date).
