In this work, an automated statistical approach for the condition monitoring of a fluid power system based on a process sensor network is presented. In a multistep process, raw sensor data are processed by feature extraction, selection and dimensional reduction and finally mapped to discriminant functions which allow the detection and quantification of fault conditions. Experimentally obtained training data are used to evaluate the impact of temperature and different aeration levels of the hydraulic fluid on the detection of pump leakage and a degraded directional valve switching behavior. Furthermore, a robust detection of the loading state of the installed filter element and an estimation of the particle contamination level is proposed based on the same analysis concept.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:29408 |
| Date | January 2016 |
| Creators | Helwig, Nikolai, Schütze, Andreas |
| 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. 3, pp. 425-436 |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | urn:nbn:de:bsz:14-qucosa-197655, qucosa:29251 |
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