Forecast Model Update Based on a Real-Time Data Processing Lambda Architecture for Estimating Partial Discharges in Hydrogenerator.
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ABSTRACT:
SUBMITTER: Pereira FH
PROVIDER: S-EPMC7765954 | biostudies-literature | 2020 Dec
REPOSITORIES: biostudies-literature
Publications
Forecast Model Update Based on a Real-Time Data Processing Lambda Architecture for Estimating Partial Discharges in Hydrogenerator.
Pereira Fabio Henrique FH Bezerra Francisco Elânio FE Oliva Diego D Souza Gilberto Francisco Martha de GFM Chabu Ivan Eduardo IE Santos Josemir Coelho JC Junior Shigueru Nagao SN Nabeta Silvio Ikuyo SI
Sensors (Basel, Switzerland) 20201217 24
The prediction of partial discharges in hydrogenerators depends on data collected by sensors and prediction models based on artificial intelligence. However, forecasting models are trained with a set of historical data that is not automatically updated due to the high cost to collect sensors' data and insufficient real-time data analysis. This article proposes a method to update the forecasting model, aiming to improve its accuracy. The method is based on a distributed data platform with the lam ...[more]
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