Application of ICEEMDAN Energy Entropy and AFSA-SVM for Fault Diagnosis of Hoist Sheave Bearing.
Zi-ming KouFen YangJuan WuTengyu LiPublished in: Entropy (2020)
Keyphrases
- fault diagnosis
- monitoring and fault diagnosis
- steam turbine
- soft computing methods
- fault detection
- expert systems
- neural network
- bp neural network
- support vector machine svm
- failure diagnosis
- support vector
- fuzzy logic
- electronic equipment
- chemical process
- rbf neural network
- electrical power systems
- fault identification
- rotating machinery
- condition monitoring
- gas turbine
- support vector machine
- feature selection
- fault detection and diagnosis
- training set
- knn
- decision making
- tennessee eastman
- real time
- multi sensor information fusion