Improving clustering based anomaly detection with concave hull: An application in fault diagnosis of wind turbines.
Peng LiOliver NiggemannPublished in: INDIN (2016)
Keyphrases
- anomaly detection
- wind turbine
- fault diagnosis
- intrusion detection
- wind speed
- anomalous behavior
- wind farm
- expert systems
- wind power
- neural network
- fault detection
- fuzzy logic
- detecting anomalies
- power generation
- operating conditions
- network intrusion detection
- intrusion detection system
- power plant
- fault detection and diagnosis
- network traffic
- one class support vector machines
- bp neural network
- multiple faults
- network security
- rbf neural network
- monitoring and fault diagnosis
- chemical process
- network anomaly detection
- negative selection algorithm
- fault identification
- clustering analysis
- normal behavior
- soft computing
- short term
- unsupervised learning
- pattern recognition