A feature extraction method based on probabilistic Principal components analysis and sampling importance resampling for bearing fault detection.
Yixiang HuangYanming LiChengliang LiuXiao LiuPublished in: ICPHM (2017)
Keyphrases
- fault detection
- principal components analysis
- industrial processes
- fault diagnosis
- condition monitoring
- fault identification
- principal components
- fault detection and diagnosis
- covariance matrix
- fuel cell
- dimensionality reduction
- failure detection
- multivariate statistical analysis
- support vector machine
- feature extraction
- tennessee eastman
- fault localization
- data mining
- linear discriminant analysis
- machine learning
- sample size
- feature set
- fault detection and isolation
- feature vectors
- neural network
- robust fault detection
- power plant
- low dimensional
- fuzzy logic
- fault isolation
- pattern recognition