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From Dataset to Deployment: A Full-Stack Autoencoder-Based Anomaly Detection Pipeline for Gas Turbine BLDC Fuel Pumps

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Published in:IEEE Access
Format: Online Article RSS Article
Published: 2026
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discipline_display Technology & Engineering
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spellingShingle From Dataset to Deployment: A Full-Stack Autoencoder-Based Anomaly Detection Pipeline for Gas Turbine BLDC Fuel Pumps
Computer Sciience
Technology & Engineering — Computing
Technology & Engineering
sub_discipline_display Technology & Engineering — Computing
sub_discipline_facet Technology & Engineering — Computing
subject_display Computer Sciience
Technology & Engineering — Computing
Technology & Engineering
Computer Sciience
Technology & Engineering — Computing
Technology & Engineering
subject_facet Computer Sciience
Technology & Engineering — Computing
Technology & Engineering
title From Dataset to Deployment: A Full-Stack Autoencoder-Based Anomaly Detection Pipeline for Gas Turbine BLDC Fuel Pumps
title_auth From Dataset to Deployment: A Full-Stack Autoencoder-Based Anomaly Detection Pipeline for Gas Turbine BLDC Fuel Pumps
title_full From Dataset to Deployment: A Full-Stack Autoencoder-Based Anomaly Detection Pipeline for Gas Turbine BLDC Fuel Pumps
title_fullStr From Dataset to Deployment: A Full-Stack Autoencoder-Based Anomaly Detection Pipeline for Gas Turbine BLDC Fuel Pumps
title_full_unstemmed From Dataset to Deployment: A Full-Stack Autoencoder-Based Anomaly Detection Pipeline for Gas Turbine BLDC Fuel Pumps
title_short From Dataset to Deployment: A Full-Stack Autoencoder-Based Anomaly Detection Pipeline for Gas Turbine BLDC Fuel Pumps
title_sort from dataset to deployment: a full-stack autoencoder-based anomaly detection pipeline for gas turbine bldc fuel pumps
topic Computer Sciience
Technology & Engineering — Computing
Technology & Engineering
url http://ieeexplore.ieee.org/document/11480846