Probabilistic neural networks for validation of on-board jet engine data

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dc.contributor.author Mathioudakis, K en
dc.contributor.author Romessis, C en
dc.date.accessioned 2014-03-01T02:42:55Z
dc.date.available 2014-03-01T02:42:55Z
dc.date.issued 2004 en
dc.identifier.issn 0954-4100 en
dc.identifier.uri http://hdl.handle.net/123456789/31141
dc.relation.uri http://www.scopus.com/inward/record.url?eid=2-s2.0-8644224949&partnerID=40&md5=372f765de7fbb46e380882eb5ab56016 en
dc.subject Artificial intelligence en
dc.subject Gas turbine diagnostics en
dc.subject Probabilistic neural networks en
dc.subject Probabilistic pattern recognition en
dc.subject Sensor faults en
dc.subject.classification Engineering, Aerospace en
dc.subject.classification Engineering, Mechanical en
dc.subject.other Backpropagation en
dc.subject.other Functions en
dc.subject.other Kalman filtering en
dc.subject.other Neural networks en
dc.subject.other Pattern recognition en
dc.subject.other Pressure effects en
dc.subject.other Probability distributions en
dc.subject.other Sensors en
dc.subject.other Turbomachinery en
dc.subject.other Gas turbine diagnostics en
dc.subject.other Probabilistic neural networks en
dc.subject.other Probabilistic pattern recognition en
dc.subject.other Sensor faults en
dc.subject.other Jet engines en
dc.title Probabilistic neural networks for validation of on-board jet engine data en
heal.type conferenceItem en
heal.language English en
heal.publicationDate 2004 en
heal.abstract A method is presented for identification of faults in the readings of sensors used to monitor the performance and the condition of jet engines. Probabilistic neural networks are used to detect the presence and identify the location and magnitude of faults (biases) in sensor readings. The faults can be detected on sets comprising a limited number of instruments, typical of those available for on-board monitoring of jet engines. An engine performance model is used to support the constitution of a network. Training information is built using the model to produce data for a comprehensive set of healthy and faulty situations. The network performance in detecting and quantifying sensor faults is validated on a large number of fault cases, also generated by a model, which are used for testing the network and cover a wide range of conditions that can be encountered in practice. An engine, representative of current large civil engine designs (large bypass, partially mixed turbofan), serves as the test vehicle for demonstration of the way the method is materialized. en
heal.journalName Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering en
dc.identifier.isi ISI:000221725300005 en
dc.identifier.volume 218 en
dc.identifier.issue 1 en
dc.identifier.spage 59 en
dc.identifier.epage 72 en

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