A novel sample selection approach based universal unsupervised domain adaptation for fault diagnosis of rotating machinery.
Biliang LuYingjie ZhangZhaohua LiuHua-Liang WeiQingshuai SunPublished in: Reliab. Eng. Syst. Saf. (2023)
Keyphrases
- rotating machinery
- fault diagnosis
- sample selection
- active learning
- fault detection
- training data
- expert systems
- neural network
- fuzzy logic
- selection strategy
- support vector machine
- condition monitoring
- bp neural network
- operating conditions
- multiple faults
- monitoring and fault diagnosis
- electrical power systems
- fault detection and diagnosis
- industrial systems
- gas turbine
- electronic equipment
- power transformers
- analog circuits
- data sets
- power plant
- multi sensor information fusion
- svm classification
- machine learning
- chemical process
- fault identification
- learning algorithm
- feature selection
- information fusion
- decision making
- learning environment