Structure identification of fuzzy model
Fuzzy Sets and Systems
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Machine Learning
Artificial Intelligence in Medicine
Optimal MLP neural network classifier for fault detection of three phase induction motor
Expert Systems with Applications: An International Journal
Vibration based fault diagnosis of monoblock centrifugal pump using decision tree
Expert Systems with Applications: An International Journal
Bearing fault diagnosis using multi-scale entropy and adaptive neuro-fuzzy inference
Expert Systems with Applications: An International Journal
Robust condition monitoring for early detection of broken rotor bars in induction motors
Expert Systems with Applications: An International Journal
Application of multiclass support vector machines for fault diagnosis of field air defense gun
Expert Systems with Applications: An International Journal
SVM practical industrial application for mechanical faults diagnostic
Expert Systems with Applications: An International Journal
Intelligent fault inference for rotating flexible rotors using Bayesian belief network
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
The build of a new non-dimensional indicator for fault diagnosis in rotating machinery
International Journal of Wireless and Mobile Computing
Wind turbine pitch faults prognosis using a-priori knowledge-based ANFIS
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Hi-index | 12.07 |
This paper presents a fault diagnosis method based on adaptive neuro-fuzzy inference system (ANFIS) in combination with decision trees. Classification and regression tree (CART) which is one of the decision tree methods is used as a feature selection procedure to select pertinent features from data set. The crisp rules obtained from the decision tree are then converted to fuzzy if-then rules that are employed to identify the structure of ANFIS classifier. The hybrid of back-propagation and least squares algorithm are utilized to tune the parameters of the membership functions. In order to evaluate the proposed algorithm, the data sets obtained from vibration signals and current signals of the induction motors are used. The results indicate that the CART-ANFIS model has potential for fault diagnosis of induction motors.