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dc.contributor.authorLau, Richard
dc.contributor.authorBagchi, Anindo
dc.contributor.authorShen, John
dc.contributor.authorTriolo, Tony
dc.contributor.authorSanchez, Kenneth
dc.contributor.authorYao, Lihan
dc.contributor.authorKovarskiy, Jacob
dc.contributor.authorCastro, Roberto
dc.date.accessioned2022-11-24T01:32:50Z
dc.date.available2022-11-24T01:32:50Z
dc.date.issued2022-10
dc.identifier.citationLau, R., Bagchi, A., Shen, J., Triolo, T., Sanchez, K., Yao, L., Kovarskiy, J., & Castro, R. (2022). Advanced Multi-Variate Time Series Analytic Techniques (Attends). International Telemetering Conference Proceedings, 57.
dc.identifier.issn1546-2188
dc.identifier.issn0884-5123
dc.identifier.issn0074-9079
dc.identifier.urihttp://hdl.handle.net/10150/666978
dc.description.abstractWe describe an advanced architecture supporting fast decisions by using multi-variate time series analytic techniques on voluminous datasets that were previously inaccessible. The system, Advanced Multi-Variate Time Series Analytic Techniques (ATTENDS) automates data ingestion, knowledge extraction, and Artificial Intelligence/Machine Learning (AI/ML) algorithm configuration for anomaly detection, failure prediction, causal analysis, and diagnosis. To enable reusability, ATTENDS presents a set of Application Programming Interfaces (API) to support user configurability and remote invocation. The APIs implement state-of-the art AI/ML algorithms for predictive maintenance, sensor component correlation for problem diagnosis, and unsupervised learning of sensor measurement anomaly for support of automated testing and evaluation. We will present two use cases including prediction of Remaining Useful Life (RUL) of Turbofan [1] and sensor diagnosis and recommendation for maintenance actions, as well as detection and quantification of target location error in an airborne platform.
dc.description.sponsorshipInternational Foundation for Telemetering
dc.language.isoen
dc.publisherInternational Foundation for Telemetering
dc.relation.urlhttp://www.telemetry.org/
dc.rightsCopyright © held by the author; distribution rights International Foundation for Telemetering
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.titleAdvanced Multi-Variate Time Series Analytic Techniques (Attends)
dc.typeProceedings
dc.typetext
dc.contributor.departmentPeraton Labs, Test Resources Management Center
dc.identifier.journalInternational Telemetering Conference Proceedings
dc.description.collectioninformationProceedings from the International Telemetering Conference are made available by the International Foundation for Telemetering and the University of Arizona Libraries. Visit https://telemetry.org/contact-us/ if you have questions about items in this collection.
dc.eprint.versionFinal published version
dc.source.journaltitleInternational Telemetering Conference Proceedings
refterms.dateFOA2022-11-24T01:32:50Z


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