A novel unsupervised anomaly detection for gas turbine using Isolation Forest.
Shisheng ZhongSong FuLin LinXuyun FuZhiquan CuiRui WangPublished in: ICPHM (2019)
Keyphrases
- gas turbine
- unsupervised anomaly detection
- anomaly detection
- semi supervised
- intrusion detection
- fault diagnosis
- fault tree
- operating conditions
- pattern mining
- fault detection
- unsupervised learning
- data mining
- real world
- neural network
- pattern recognition
- soft computing
- multi dimensional
- supervised learning
- case based reasoning
- decision making