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dc.contributor.authorLosik, Len
dc.contributor.authorWahl, Sheila
dc.contributor.authorOwen, Lewis
dc.date.accessioned2016-06-01T18:00:48Z
dc.date.available2016-06-01T18:00:48Z
dc.date.issued1996-10
dc.identifier.issn0884-5123
dc.identifier.issn0074-9079
dc.identifier.urihttp://hdl.handle.net/10150/611451
dc.descriptionInternational Telemetering Conference Proceedings / October 28-31, 1996 / Town and Country Hotel and Convention Center, San Diego, Californiaen_US
dc.description.abstractThis paper addresses research completed for predicting hardware failures and estimating remaining service life for satellite components using a Failure Prediction Process (FPP). It is a joint paper, presenting initial research completed at the University of California, Berkeley, Center for Extreme Ultraviolet (EUV) Astrophysics using telemetry from the EUV EXPLORER (EUVE) satellite and statistical computation analysis completed by Lockheed Martin. This work was used in identifying suspect "failure precursors." Lockheed Martin completed an exploration into the application of statistical pattern recognition methods to identify FPP events observed visually by the human expert. Both visual and statistical methods were successful in detecting suspect failure precursors. An estimate for remaining service life for each unit was made from the time the suspect failure precursor was identified. It was compared with the actual time the equipment remained operable. The long-term objective of this research is to develop a resident software module which can provide information on FPP events automatically, economically, and with high reliability for long-term management of spacecraft, aircraft, and ground equipment. Based on the detection of a Failure Prediction Process event, an estimate of remaining service life for the unit can be calculated and used as a basis to manage the failure.
dc.description.sponsorshipInternational Foundation for Telemeteringen
dc.language.isoen_USen
dc.publisherInternational Foundation for Telemeteringen
dc.relation.urlhttp://www.telemetry.org/en
dc.rightsCopyright © International Foundation for Telemeteringen
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectTelemetryen
dc.subjectAnalysisen
dc.subjectAnomaly Resolutionen
dc.subjectFailure Predictionen
dc.subjectAnomaly Predictionen
dc.subjectStatistical Pattern Recognitionen
dc.titlePredicting Failures and Estimating Duration of Remaining Service Life from Satellite Telemetryen_US
dc.typetexten
dc.typeProceedingsen
dc.contributor.departmentLockheed Martin Telemetry & Instrumentationen
dc.contributor.departmentLockheed Martin Advanced Technology Centeren
dc.identifier.journalInternational Telemetering Conference Proceedingsen
dc.description.collectioninformationProceedings from the International Telemetering Conference are made available by the International Foundation for Telemetering and the University of Arizona Libraries. Visit http://www.telemetry.org/index.php/contact-us if you have questions about items in this collection.en
refterms.dateFOA2018-06-17T18:49:07Z
html.description.abstractThis paper addresses research completed for predicting hardware failures and estimating remaining service life for satellite components using a Failure Prediction Process (FPP). It is a joint paper, presenting initial research completed at the University of California, Berkeley, Center for Extreme Ultraviolet (EUV) Astrophysics using telemetry from the EUV EXPLORER (EUVE) satellite and statistical computation analysis completed by Lockheed Martin. This work was used in identifying suspect "failure precursors." Lockheed Martin completed an exploration into the application of statistical pattern recognition methods to identify FPP events observed visually by the human expert. Both visual and statistical methods were successful in detecting suspect failure precursors. An estimate for remaining service life for each unit was made from the time the suspect failure precursor was identified. It was compared with the actual time the equipment remained operable. The long-term objective of this research is to develop a resident software module which can provide information on FPP events automatically, economically, and with high reliability for long-term management of spacecraft, aircraft, and ground equipment. Based on the detection of a Failure Prediction Process event, an estimate of remaining service life for the unit can be calculated and used as a basis to manage the failure.


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