A Semi-Supervised Autoencoder With an Auxiliary Task (SAAT) for Power Transformer Fault Diagnosis Using Dissolved Gas Analysis.
Sunuwe KimSoo-Ho JoWongon KimJongmin ParkJingyo JeongYeongmin HanDaeil KimByeng Dong YounPublished in: IEEE Access (2020)
Keyphrases
- power transformers
- fault diagnosis
- semi supervised
- condition monitoring
- neural network
- expert systems
- gas turbine
- fault detection
- bp neural network
- fault tree
- fault detection and isolation
- operating conditions
- fault detection and diagnosis
- fuzzy logic
- genetic algorithm
- rbf neural network
- multiple faults
- fault identification
- electronic equipment
- rotating machinery
- monitoring and fault diagnosis
- labeled data
- chemical process
- machine learning
- real time
- analog circuits
- computational intelligence
- training data
- multi sensor information fusion