Fault Detection of Bearing: An Unsupervised Machine Learning Approach Exploiting Feature Extraction and Dimensionality Reduction.
Lucas Costa BritoGian Antonio SustoJorge Nei BritoMarcus Antonio Viana DuartePublished in: Informatics (2021)
Keyphrases
- fault detection
- dimensionality reduction
- feature extraction
- machine learning
- discriminant projection
- unsupervised learning
- feature selection
- pattern recognition
- industrial processes
- fault diagnosis
- principal component analysis
- linear discriminant analysis
- manifold learning
- low dimensional
- supervised learning
- condition monitoring
- supervised classification
- dimensionality reduction methods
- feature space
- failure detection
- fault identification
- tennessee eastman
- robust fault detection
- fault detection and diagnosis
- dimension reduction
- high dimensional
- discriminant analysis
- data mining
- feature vectors
- principal components
- unsupervised feature selection
- power plant
- semi supervised
- fuel cell
- artificial intelligence
- fault isolation
- face recognition
- high dimensional data
- fault detection and isolation
- image processing
- support vector machine
- extracted features
- expert systems
- decision making
- artificial neural networks
- kernel pca
- neural network