In:
Annual Conference of the PHM Society, PHM Society, Vol. 11, No. 1 ( 2019-09-22)
Abstract:
This paper examines diagnostics and prognostics of Lithium-Polymer (Li-Po) batteries for unmanned aerial vehicles (UAVs). Several discharge voltage histories obtained during actual indoor flights constitute the training data for a data-driven approach, utilizing the Non-Homogenous Hidden Semi Markov model (NHHSMM). NHHSMM is a suitable candidate as it has a rich mathematical structure, which is capable of describing the discharge process of Li-Po batteries and providing diagnostic and prognostic measures. Diagnostics and prognostics in unseen data are obtained and compared with the actual remaining flight time in order to validate the effectiveness of the selected model.
Type of Medium:
Online Resource
ISSN:
2325-0178
,
2325-0178
DOI:
10.36001/phmconf.2019.v11i1.785
Language:
Unknown
Publisher:
PHM Society
Publication Date:
2019
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