Introduction to computational intelligence paradigms
Computational intelligence in games
Intelligent systems: architectures and perspectives
Recent advances in intelligent paradigms and applications
Detection of stator winding fault in induction motor using fuzzy logic
Applied Soft Computing
Feature-based classifier ensembles for diagnosing multiple faults in rotating machinery
Applied Soft Computing
Broken Rotor Bars Fault Detection in Induction Motors Using Park's Vector Modulus and FWNN Approach
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II
FDI based on pattern recognition using Kalman prediction: Application to an induction machine
Engineering Applications of Artificial Intelligence
Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference
Expert Systems with Applications: An International Journal
Automatic detection and classification of rotor cage faults in squirrel cage induction motor
Neural Computing and Applications
Induction motor fault detection and diagnosis using a current state space pattern recognition
Pattern Recognition Letters
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs)
Applied Soft Computing
Automated Fault Detection and Diagnosis in Mechanical Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Neural Networks
Fuzzy min-max neural networks. I. Classification
IEEE Transactions on Neural Networks
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In this paper, a hybrid soft computing model comprising the Fuzzy Min-Max (FMM) neural network and the Classification and Regression Tree (CART) for motor fault detection and diagnosis is described. Specifically, the hybrid model, known as FMM-CART, is used to detect and classify fault conditions of induction motors in both offline and online environments. A series of experiments is conducted, whereby the Motor Current Signature Analysis (MCSA) method is applied to form a database containing stator current signatures under different motor conditions. The signal harmonics from the power spectral density (PSD) are extracted, and used as the discriminative input features for fault classification with FMM-CART. Three main induction motor conditions, viz. broken rotor bars, stator winding faults, and unbalanced supply, are used to evaluate the effectiveness of FMM-CART. The results indicate that FMM-CART is able to detect motor faults in the early stage, in order to avoid further damage to the induction motor as well as the overall machine or system that uses the motor in its operations.