A Novel Rolling Bearing Fault Diagnosis Method Based on Adaptive Feature Selection and Clustering.
Jingbao HouYunxin WuAbdulrahaman Shuaibu AhmadHai GongLei LiuPublished in: IEEE Access (2021)
Keyphrases
- feature weighting
- feature selection
- text categorization
- clustering method
- unsupervised learning
- clustering algorithm
- high dimensionality
- k means
- data mining and pattern recognition
- monitoring and fault diagnosis
- hierarchical clustering
- feature space
- self organizing maps
- class separability
- unsupervised feature selection
- graph theoretic
- feature selection algorithms
- support vector machine
- data sets
- data pre processing
- small sample
- data points
- multi task
- mutual information
- data clustering
- multi class
- cluster analysis
- information theoretic
- outlier detection
- mathematical model
- high dimensional data
- text mining
- feature set