Fault Detection of Hydroelectric Generators by Robust Random Cut Forest with Feature Selection Using Hilbert-Schmidt Independence Criterion.
Yuki HaraYoshikazu FukuyamaKiyo AraiYuichi ShimasakiYuto OsadaKenya MurakamiTatsuya IizakaTetsuro MatsuiPublished in: SmartIoT (2021)
Keyphrases
- fault detection
- feature selection
- robust fault detection
- power plant
- hilbert schmidt independence criterion
- fault diagnosis
- fault identification
- industrial processes
- tennessee eastman
- failure detection
- machine learning
- feature space
- fault detection and diagnosis
- condition monitoring
- genetic algorithm
- decision support system
- fuel cell
- text categorization
- decision making
- expert systems
- support vector
- neural network
- control system
- learning algorithm
- feature extraction
- fault localization
- training set
- intelligent systems
- text classification
- feature set
- classification accuracy