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International Journal on Interactive Design and Manufacturing (IJIDeM)

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02.08.2020 | Technical Paper

A methodology for detection of wear in hydraulic axial piston pumps

verfasst von: Jessica Gissella Maradey Lázaro, Carlos Borrás Pinilla

Erschienen in: International Journal on Interactive Design and Manufacturing (IJIDeM) | Ausgabe 3/2020

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Abstract

An effective asset management has a direct impact on maintenance costs, reliability, and equipment availability, especially in hydraulic machinery. Variable displacement axial piston pump is a major component used in the industry due to its load capacity ratio, pressure management, and high performance. Some of the main faults are wear and abrasion of the valve plates, increasing pressure losses as well as temperature, decreasing volumetric efficiency, and abnormal vibration. The off-line methodology implemented includes preprocessing of the vibration signals taken from the test bench available for this study, the feature extraction using wavelets, a stage of detection and classification through the use of artificial neural networks. Several networks were assessment, such as Adaline, nonlinear, and multilayer perceptron networks. Classification percentages greater than 90% are obtained taking into consideration 5 wear conditions related to the loss of volumetric efficiency.

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Casoli, P., Bedotti, A., Campanini, F., Pastori, M.: A methodology based on cyclostationary analysis for fault detection of hydraulic axial piston pumps. Energies 11(7), 1874 (2018)CrossRef

Maradey, J., Borrás, C., Roa, S.: A survey of approaches for fault diagnosis in axial piston pumps. Int. Mech. Eng. Cong. Expos. IMECE ASME 4, 50521 (2016)

Watton, J.: Modelling, Monitoring, and Diagnostic Techniques for Fluid Power Systems. Springer, Berlin (2007)

Gao, Y., Zhang, Q., Kong, X.: Wavelet-based pressure analysis for hydraulic pump health diagnosis. Trans. ASAE 46(4), 969 (2003)

Hu, W., Wang, S., He, Z., Zhao, S.: Prognostic analysis based on an updated grey model for axial piston pump. In: 2010 5th IEEE Conference on Industrial Electronics and Applications, pp. 571–575. IEEE, New York (2010)

Palazzolo, J.J., Scheunemann, L.D., Hartin, J.R.: Leakage fault detection method for axial-piston variable displacement pumps. In: 2008 IEEE Aerospace Conference, March, pp. 1–8. IEEE, New York (2008)

Canbulut, F., Sinanoglu, C.: An investigation on the performance of hydrostatic pumps using artificial neural networks. JSME Int. J. Ser. C Mech. Syst. Mach. Elem. Manuf. 47(3), 864–872 (2004)CrossRef

Bo, C., Guoping, Y., Dongshi, X., Junhao, G.: Modeling and simulation of axial piston hydraulic pump. In: 2011 3rd International Conference on Measuring Technology and Mechatronics Automation, vol. 2, pp. 609-612. IEEE, New York (2011)

Lurette, C., Lecoeuche, S.: Unsupervised and auto-adaptive neural architecture for on-line monitoring. Application to a hydraulic process. Eng. Appl. Artif. Intell. 16(5–6), 441–451 (2003)MATHCrossRef

10.

Ramdén, T., Krus, P., Palmerg, J.O.: Reliability, and sensitivity analysis of a condition monitoring technique. In: Proceedings of the JFPS International Symposium on Fluid Power. The Japan Fluid Power System Society, vol. 3, pp. 567–57 (1996)

11.

Tan Guanzeheng, L.Z., Yang H.: Fault diagnosis of hydraulic pumps using an improved neural network. J. CSUT (J. Cent. South Univ. Technol.) 2(1), 65–68 (1995)

12.

Gao, Y., Kong, X., Zhang, Q.: Wavelet approach for performance monitoring and diagnosis of a hydraulic pump. In: Proceedings of the JFPS International Symposium on Fluid Power, The Japan Fluid Power System Society, vol. 6, pp. 711–716 (2005)

13.

Hou, W., Lu, C., Liu, H., Lu, C.: Fault diagnosis based on wavelet package for hydraulic pump. In: 2009 8th International Conference on Reliability, Maintainability and Safety, pp. 831–835. IEEE, New York (2009)

14.

Siyuan, L., Linlin, D., Wanlu, J.: Study on application of principal component analysis to fault detection in hydraulic pump. In: Proceedings of 2011 International Conference on Fluid Power and Mechatronics, pp. 173–178. IEEE, New York (2011)

15.

He, Z., Wang, S., Wang, K., Li, K.: Prognostic analysis based on hybrid prediction method for axial piston pump. In: IEEE 10th International Conference on Industrial Informatics, pp. 688–692. IEEE, New York (2012)

16.

Du, J., Wang, S., Zhang, H.: Layered clustering multi-fault diagnosis for hydraulic piston pump. Mech. Syst. Signal Process. 36(2), 487–504 (2013)CrossRef

17.

Kivelä, T., Mattila, J.: Internal leakage fault detection for variable displacement axial piston pump. In: ASME/BATH 2013 Symposium on Fluid Power and Motion Control, American Society of Mechanical Engineers Digital Collection (2013)

18.

Jikun, B., Chen, L., Zhipeng, W., Zili, W.: An approach to performance assessment and fault diagnosis for hydraulic pumps. J. Vibroeng. 16(3), 1444–1454 (2014)

19.

Salimi, E., Niromandfam, B.: Condition monitoring of hydraulic pump by wavelet transform and artificial neural network. JNAS Trans. J. Nov. Appl. Sci. 2015, 1123–1128 (2015)

20.

Wen, L., Li, X., Gao, L., Zhang, Y.: A new convolutional neural network-based data-driven fault diagnosis method. IEEE Trans. Ind. Electron. 65(7), 5990–5998 (2017)CrossRef

21.

Lan, Y., Hu, J., Huang, J., Niu, L., Zeng, X., Xiong, X., Wu, B.: Fault diagnosis on slipper abrasion of axial piston pump based on extreme learning machine. Measurement 124, 378–385 (2018)CrossRef

22.

Wang, S., Xiang, J., Zhong, Y., Tang, H.: A data indicator-based deep belief networks to detect multiple faults in axial piston pumps. Mech. Syst. Signal Process. 112, 154–170 (2018)CrossRef

23.

Özmen, Ö., Sınanoğlu, C., Batbat, T., Güven, A.: Prediction of slipper pressure distribution and leakage behaviour in axial piston pumps using ANN and MGGP. Math. Probl. Eng. 2019, 13 (2019)CrossRef

24.

Wang, S., Xiang, J.: A minimum entropy deconvolution-enhanced convolutional neural networks for fault diagnosis of axial piston pumps. Soft. Comput. 24(4), 2983–2997 (2020)CrossRef

25.

Gao, Q., Tang, H., Xiang, J., Zhong, Y., Ye, S., Pang, J.: A Walsh transform-based Teager energy operator demodulation method to detect faults in axial piston pumps. Measurement 134, 293–306 (2019)CrossRef

26.

Peng, Z.K., Chu, F.L.: Application of the wavelet transforms in machine condition monitoring and fault diagnostics: a review with bibliography. Mech. Syst. Signal Process. 18(2), 199–221 (2004)CrossRef

27.

Tao, X., Wang, Z., Ma, J., Fan, H.: 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, New York (2012)

28.

Karkoub, M.A., Gad, O.E., Rabie, M.G.: Predicting axial piston pump performance using neural networks. Mech. Mach. Theory 34(8), 1211–1226 (1999)CrossRef

29.

Bayer, C., Enge-Rosenblatt, O.: Modeling of hydraulic axial piston pumps including specific signs of wear and tear. In: Proceedings of the 8th International Modelica Conference; March 20th–22nd. Technical University, Dresden. Linköping University Electronic Press. No. 63, pp. 461–466 (2011)

30.

Chu, M.H., Kang, Y., Chang, Y.F., Liu, Y.L., Chang, C.W.: Model-following controller based on a neural network for variable displacement pump. JSME Int J. Ser. C 46(1), 176–187 (2003)CrossRef

31.

Aly, A.: A: an artificial neural network flow control of variable displacement piston pump with pressure compensation. Int. J. Control Autom. Syst. 4(1), 1–7 (2015)MathSciNet

32.

Bolaño, Y., Niño, J.: Rediseño y construcción de un banco de pruebas para diagnóstico de fallas en una hidrotransmisión bomba variable-motor fijo. Published Bachelor Thesis. Universidad Industrial de Santander, Bucaramanga, COL (2016)

33.

Arizmendi, C.J.: Signal processing techniques for brain tumor diagnosis from magnetic resonance spectroscopy data. Doctoral Dissertation, Ph.D. Thesis. Universitat Politecnica de Catalunya, Spain (2012)

34.

Pinto, J.: Análisis e interpretación de señales cardiorrespiratorias para determinar el momento óptimo de desconexión de un paciente asistido mediante ventilación. Published Bachelor Thesis. Universidad Autónoma de Bucaramanga, Bucaramanga, COL (2014)

35.

Arango, G.: Clasificación de fallas en motores eléctricos utilizando señales de vibración. Published Thesis Master. Universidad Tecnológica de Pereira, Pereira, COL (2007)

36.

Ortiz, J.: Análisis de la interacción entre el patrón respiratorio y la frecuencia cardíaca, en pacientes asistidos mediante ventilación mecánica, para la estimación del momento óptimo en la extubación. Published Bachelor Thesis. Universidad Autónoma de Bucaramanga, Bucaramanga, COL (2014)

37.

Borrás, C., Stalford, H.L.: Pattern recognition in hydraulic backlash using neural network. In: Proceedings of the 2002 American Control Conference (IEEE Cat. No. CH37301), vol. 1, pp. 400–405 Anchorage (2002)

38.

Maradey, J., Borrás, C.: Detection and classification of wear fault in axial piston pumps: using ANNs and pressure signals. IGI Global, pp. 286–336 (2020)

Titel: A methodology for detection of wear in hydraulic axial piston pumps
verfasst von: Jessica Gissella Maradey Lázaro
Carlos Borrás Pinilla
Publikationsdatum: 02.08.2020
Verlag: Springer Paris
Erschienen in: International Journal on Interactive Design and Manufacturing (IJIDeM) / Ausgabe 3/2020
Print ISSN: 1955-2513
Elektronische ISSN: 1955-2505
DOI: https://doi.org/10.1007/s12008-020-00681-w

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