Bearing fault detection based on interval type-2 fuzzy logic systems for support vector machines

A method based on Interval Type-2 Fuzzy Logic Systems (IT2FLSs) for combination of different Support Vector Machines (SVMs) in order to bearing fault detection is the main argument of this paper. For this purpose, an experimental setup has been provided to collect data samples of stator current phase a of the induction motor using healthy and defective bearing. The defective bearing has an inner race hole with the diameter 1-mm that is created by the spark. An Interval Type-2 Fuzzy Fusion Model (IT2FFM) has been presented that is consists of two phases. Using this IT2FFM, testing data samples have been classified. A comparison between T1FFM, IT2FFM, SVMs and also Adaptive Neuro Fuzzy Inference Systems (ANFIS) in classification of testing data samples has been done and the results show the effectiveness of the proposed ITFFM.