A Bayesian approach to fault identification in the presence of multi-component degradation

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dc.contributor.authorLin, Yufei
dc.contributor.authorZakwan, Skaf
dc.contributor.authorJennions, Ian K.
dc.date.accessioned2017-03-23T11:14:28Z
dc.date.available2017-03-23T11:14:28Z
dc.date.issued2017-03-10
dc.description.abstractFault diagnosis typically consists of fault detection, isolation and identification. Fault detection and isolation determine the presence of a fault in a system and the location of the fault. Fault identification then aims at determining the severity level of the fault. In a practical sense, a fault is a conditional interruption of the system ability to achieve a required function under specified operating condition; degradation is the deviation of one or more characteristic parameters of the component from acceptable conditions and is often a main cause for fault generation. A fault occurs when the degradation exceeds an allowable threshold. From the point a new aircraft takes off for the first time all of its components start to degrade, and yet in almost all studies it is presumed that we can identify a single fault in isolation, i.e. without considering multi-component degradation in the system. This paper proposes a probabilistic framework to identify a single fault in an aircraft fuel system with consideration of multi-component degradation. Based on the conditional probabilities of sensor readings for a specific fault, a Bayesian method is presented to integrate distributed sensory information and calculate the likelihood of all possible fault severity levels. The proposed framework is implemented on an experimental aircraft fuel rig which illustrates the applicability of the proposed method.en_UK
dc.identifier.citationYufei Lin, Skaf Zakwan, and Ian Jennions, A Bayesian approach to fault identification in the presence of multi-component degradation. International Journal of Prognostics and Health Management, Volume 8. Available online 10 March 2017, publication number 004en_UK
dc.identifier.cris17090514
dc.identifier.issn2153-2648
dc.identifier.urihttps://www.phmsociety.org/node/2124
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/11645
dc.language.isoenen_UK
dc.publisherPHM Societyen_UK
dc.rightsAttribution 3.0 Unported (CC BY 3.0). You are free to: Share — copy and redistribute the material in any medium or format Adapt — remix, transform, and build upon the material for any purpose, even commercially. The licensor cannot revoke these freedoms as long as you follow the license terms. Under the following terms: Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. Information: No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
dc.subjectFault identificationen_UK
dc.subjectBayesian methoden_UK
dc.subjectMulti-component degradationen_UK
dc.subjectAircraft fuel rigen_UK
dc.titleA Bayesian approach to fault identification in the presence of multi-component degradationen_UK
dc.typeArticleen_UK

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