Fault Detection and Diagnosis Based on Unsupervised Machine Learning Methods: A Kaplan Turbine Case Study

Fault Detection and Diagnosis Based on Unsupervised Machine Learning Methods: A Kaplan Turbine Case Study

From the breakdown of the Kaplan rotor of a hydrogenerator unit and the monitored data collected during its operation before such a failure, this work presents a post-occurrence data analysis in which a previously developed hybrid method based on unsupervised machine learning techniques is applied t...

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Bibliographic Details
Main Authors: Miguel A. C. Michalski, Arthur H. A. Melani, Renan F. da Silva, Gilberto F. M. de Souza, Fernando H. Hamaji
Format: Article
Language:English
Published: MDPI AG 2021-12-01
Series:Energies
Subjects:
fault detection and diagnosis
Kaplan turbines
rotor blades failure analysis
moving window principal component analysis
MWPCA
Bayesian networks
Online Access:https://www.mdpi.com/1996-1073/15/1/80

Internet

https://www.mdpi.com/1996-1073/15/1/80

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