This paper deals with the application of speed variable pumps in industrial hydraulic systems. The benefit of the natural feedback of the load torque is investigated for the issue of condition monitoring as the development of losses can be taken as evidence of faults. A new approach is proposed to improve the fault detection capabilities by tracking the changes via machine learning techniques. The presented algorithm is an art of adaptive modeling of the torque balance over a range of steady operation in fault free behavior. The aim thereby is to form a numeric reference with acceptable accuracy of the unit used in particular, taking into consideration the manufacturing tolerances and other operation conditions differences. The learned model gives baseline for identification of major possible abnormalities and offers a fundament for fault isolation by continuously estimating and analyzing the deviations.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:29296 |
| Date | January 2016 |
| Creators | Ali, Emad, Weber, Jürgen, Wahler, Matthias |
| Contributors | Dresdner Verein zur Förderung der Fluidtechnik e. V. |
| Publisher | Technische Universität Dresden |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | doc-type:conferenceObject, info:eu-repo/semantics/conferenceObject, doc-type:Text |
| Source | 10th International Fluid Power Conference (10. IFK) March 8 - 10, 2016, Vol. 1, pp. 121-134 |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | urn:nbn:de:bsz:14-qucosa-196933, qucosa:29237 |
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