Design and development of GUI based model for fault diagnosis of induction motor using interval type-2 fuzzy and genetically tuned interval type-2 fuzzy classifier

In this paper apply interval type-2 fuzzy classifier and genetically tuned interval type-2 fuzzy classifier for diagnostics of induction motor based on spectral analysis of stator current signal. This paper is presented an approach to tune fuzzy based fault diagnosis model of induction motor using Genetic Algorithm (GA). Interval type-2 fuzzy logic controller (IT2FLC) where the fuzzy parameters, e.g. fuzzy membership functions and fuzzy rule bases are tuned by genetic algorithm (GAs) known as genetic interval type-2 fuzzy system. With the help of Matlab Simulink and GUI based KQJJ-IMFD (Kulkarni Qureshi Jha Jogi - Induction Motor Fault Diagnosis) model developed for fault diagnosis of induction motor using FFT and soft computing i.e. interval type-2 fuzzy logic system with genetic algorithm. Motor current signature analysis (MCSA) detection method is used for fault diagnosis of induction motor. All results are simulated and analyzed.