Health condition monitoring, commonly referred as Integrated Vehicle Health Management (IVHM) for fleets or vehicles, studies the current and future health state of a system. Health monitoring techniques based on data driven approaches have proven successful in several areas and are easily scalable; however they do not rely on the understating of the physics of failure; whereas Physics-based Model (PbM) approaches require expert knowledge of the failure modes and are based on the understanding of the component behaviour and degradation mechanisms. The development of IVHM is particularly challenging for legacy aircraft due to the restrictive regulations of the aerospace industry. This thesis proposes a novel PbM technique to detect metal-metal contact in hydrodynamic bearings. The planetary transmission of an aircraft’s Integrated Drive Generator (IDG) is used as a case study. Research on the detection of metal-metal contact in hydrodynamic bearings has focused on data driven approaches using vibration or acoustic emissions rather than on PbMs. The proposed technique estimates metal-metal contact by modelling the physical phenomena involved in the failure mechanism and only the speed, load and temperature are required as inputs, all of them available in the IDG and not requiring any additional sensors. The study of metal-metal in hydrodynamic bearings in the field of tribology has focused on mixed lubrication models of the whole bearing, or computational models accounting for local effect under the hydrodynamic lubrication region. In addition to the IVHM technique, this thesis contributes to the field of tribology by proposing a computational mixed lubrication model capable of studying metal-metal contact locally along the lubricated surface of the bearing. Experimental results of a plain journal bearing have been used to validate the PbM and a replica of the transmission of the IDG has been tested to evaluate the effectiveness of the proposed technique at detecting metal-metal contact.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:705049 |
| Date | January 2016 |
| Creators | Cubillo, Adrian |
| Contributors | Perinpanayagam, Suresh |
| Publisher | Cranfield University |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://dspace.lib.cranfield.ac.uk/handle/1826/11585 |
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