Predictive Diagnosis in Axial Piston Pumps A Study for High Frequency Condition Indicators Under Variable Operating Conditions

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Published Mar 25, 2023
Oliver Gnepper
Hannes Hitzer Olaf Enge-Rosenblatt

Abstract

Increasing reliability, availability and safety requirements as well as an increasing amount of data acquisition systems have enabled condition-based maintenance in mobile and industrial machinery. In this paper, we present a methodology to develop a robust diagnostic approach. This includes the consideration of variable operating conditions in the data acquisition process as well as a versatile, non domain-specific feature extraction technique. By doing so, we train anomaly detection models for different fault types and different fault intensities in variable displacement axial piston pumps. Our specific interest points to the investigation of high-frequency condition indicators with a sampling rate of 1 MHz. Furthermore, we compare those to industry standard sensors, sampled with up to 20 kHz.
By considering variable operating conditions, we are able to quantify the influence of the operating point. The results show, that high-frequency features are a suitable condition-indicator across several operating points and can be used to detect faults more easily. Although set up on a test-bench, the experimental design allows to draw conclusions about realistic field operational conditions.

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Keywords

Prognostics and Health Management, Machine Learning, Axial piston unit, Fault detection, Variable operating conditions, Condition Monitoring, Anomaly Detection, Vibration signals

References
Backe, W., & Kleinbreuer, W. (1981). Kavitation und Kavitationserosion in hydraulicschen Systemen. Konstrukteur, 12, 32–46.
Bauer, S., & Puente Leon, F. (2017). Praxis der digitalen signalverarbeitung. Karlsruhe: KIT Scientific Publishing. doi: 10.5445/KSP/1000067012
Bayer, C., & Enge-Rosenblatt, O. (2011a). Model based development of a condition monitoring system (cms) for an axial piston pump. In Tagungsband Mechatronik; march 31th- april 1st; Technical Univeristy; Dresden; Germany (pp. 175–180).
Bayer, C., & Enge-Rosenblatt, O. (2011b). 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 Univeristy; Dresden; Germany (pp. 461–466).
Biggio, L., & Kastanis, I. (2020). Prognostics and health management of industrial assets: Current progress and road ahead. Frontiers in Artificial Intelligence, 3. doi: 10.3389/frai.2020.578613
Breiman, L. (2001). Random forests. Machine learning, 45(1), 5–32. doi: 10.1023/A:1010933404324
Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000). LOF: Identifying density-based local outliers. In Proceedings of the 2000 ACM SIGMOD international conference on management of data (p. 93–104). New York, NY, USA: Association for Computing Machinery. doi: 10.1145/342009.335388
Brinkschulte, L., & Geimer, M. (2017). Echtzeitfahige Abschatzung der Restlebensdauer von Komponenten. ATZ Offhighway, 10(3), 54–61. doi: 10.1007/s35746-017-0036-1
Casoli, P., Pastori, M., & Scolari, F. (2019). A multi-fault diagnostic method based on acceleration signal for a hydraulic axial piston pump. AIP Conference Proceedings, 2191(1), 020037. doi: 10.1063/1.5138770
Cattell, R. B. (1966). The scree test for the number of factors. Multivariate Behavioral Research, 1(2), 245-276. doi: 10.1207/s15327906mbr0102\_10
Ding, Y., Ma, J., & Tian, Y. (2015). Health assessment and fault classification for hydraulic pump based on LR and softmax regression. Journal of Vibroengineering, 17(4), 1805–1816.
Du, J., Wang, S., & Zhang, H. (2013). Layered clustering multi-fault diagnosis for hydraulic piston pump. Mechanical Systems and Signal Processing, 36(2), 487-504. doi: 10.1016/j.ymssp.2012.10.020
Gomes, J. P. P., Leao, B. P., Vianna, W. O., Galvao, R. K., & Yoneyama, T. (2012). Failure prognostics of a hydraulic pump using kalman filter. Annual Conference of the PHM Society, 4(1). doi: 10.36001/phmconf.2012.v4i1.2085
Hast, D., Findeisen, R., & Streif, S. (2015). Detection and isolation of parametric faults in hydraulic pumps using a set-based approach and quantitative–qualitative fault specifications. Control Engineering Practice, 40, 61-70. doi: 10.1016/j.conengprac.2015.01.003
Helwig, N., Klein, S., & Schutze, A. (2015). Identification and quantification of hydraulic system faults based on multivariate statistics using spectral vibration features. Procedia Engineering, 120, 1225-1228. doi: 10.1016/j.proeng.2015.08.835
Hotelling, H. (1933). Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology, 24(6), 417. doi: 10.1037/h0071325
Ivantysyn, J., & Ivantysynova, M. (2003). Hydrostatic pumps and motors: principles, design, performance, modelling, analysis, control and testing. Tech Books International.
Kim, N.-H., An, D., & Choi, J.-H. (2017). Prognostics and Health Management of Engineering Systems. Cham: Springer International Publishing. doi: 10.1007/978-3-319-44742-1
Kleinbreuer, W. (1979). Untersuchung der Werkstoffzerstorung durch Kavitation in olhydraulischen Systemen : [mit Tabellen] (Doctoral dissertation, Aachen). Retrieved from https://publications.rwth-aachen.de/record/60861 (Aachen, Techn. Hochsch., Diss., 1979)
Kramer, M. A. (1991). Nonlinear principal component analysis using autoassociative neural networks. AIChE Journal, 37(2), 233-243. doi: 10.1002/aic.690370209
Lan, Y., Hu, J., Huang, J., Niu, L., Zeng, X., Xiong, X., & Wu, B. (2018). Fault diagnosis on slipper abrasion of axial piston pump based on extreme learning machine. Measurement, 124, 378-385. doi: 10.1016/j.measurement.2018.03.050
Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2008). Isolation forest. In 2008 Eighth IEEE International Conference on Data Mining (p. 413-422). doi: 10.1109/ICDM.2008.17
Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2012, mar). Isolationbased anomaly detection. ACM Transactions on Knowledge Discovery from Data, 6(1). doi: 10.1145/2133360.2133363
Mandal, N. P., Saha, R., & Sanyal, D. (2008). Theoretical simulation of ripples for different leading-side groove volumes on manifolds in fixed-displacement axial-piston pump. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 222(6), 557-570. doi: 10.1243/09596518JSCE580
Maradey Lazaro, J. G., & Borrás Pinilla, C. (2020). A methodology for detection of wear in hydraulic axial piston pumps. International Journal on Interactive Design and Manufacturing (IJIDeM), 14(3), 1103–1119. doi: 10.1007/s12008-020-00681-w
Matthews, B. (1975). Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochimica et Biophysica Acta (BBA) - Protein Structure, 405(2), 442-451. doi: 10.1016/0005-2795(75)90109-9
Michaud, D. (2014, May). https://z4y6y3m2.rocketcdn.me/blog/wp-content/uploads/2014/05/Surface-Mining-Infographic-Large.jpg. Gates. (Online, accessed 2022-06-23)
Munch, H. (2021). Akustisch erweiterte Virtualisierung von Industrieprodukten (doctoralthesis, FAU University Press). doi: 10.25593/978-3-96147-387-8
Physik Instrumente. (2012). Smart piezo mechanisms: Transducers for sensing / actuation, adaptronics for industry and automation. online, accessed: 2023-02-05. Retrieved from https://www.piezo.ws/piezo products/Piezo-Patch-Transducer/index.php
Ramden, T. (1998). ´ Condition monitoring and fault diagnosis of fluid power systems: Approaches with neural networks and parameter identification (doctoralthesis, Linkopings universitet). Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-149922
Scholkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001, 07). Estimating the Support of a High-Dimensional Distribution. Neural Computation, 13(7), 1443-1471. doi: 10.1162/089976601750264965
Siebertz, K., van Bebber, D., & Hochkirchen, T. (2017). Statistische Versuchsplanung; Design of Experiments (DoE) (2nd ed.). Springer Vieweg Berlin, Heidelberg. doi: 10.1007/978-3-662-55743-3
Torikka, T. (2011). Bewertung von Analyseverfahren zur Zustandsuberwachung am Beispiel einer Axialkolbeneinheit (doctoralthesis). RWTH Aachen University.
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Technical Papers