Title :
Bearing fault detection based on interval type-2 fuzzy logic systems for support vector machines
Author :
Zarei, Jafar ; Arefi, Mohammad Mehdi ; Hassani, Hossein
Author_Institution :
Sch. of Electr. & Electron. Eng., Shiraz Univ. of Technol., Shiraz, Iran
Abstract :
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.
Keywords :
electrical engineering computing; fault diagnosis; fuzzy logic; fuzzy neural nets; fuzzy reasoning; fuzzy set theory; induction motors; machine bearings; mechanical engineering computing; pattern classification; stators; support vector machines; ANFIS; IT2FFM; Interval Type-2 Fuzzy Fusion Model; SVM; T1FFM; adaptive neuro fuzzy inference systems; bearing fault detection; defective bearing; healthy bearing; induction motor; inner race hole; interval type-2 fuzzy logic systems; size 1 mm; stator current phase; support vector machines; testing data sample classification; Accuracy; Fault detection; Fuzzy logic; Fuzzy sets; Kernel; Support vector machines; Testing; Bearing; Fault Detection; Support Vector Machines; Type-2 fuzzy logic system;
Conference_Titel :
Modeling, Simulation, and Applied Optimization (ICMSAO), 2015 6th International Conference on
Conference_Location :
Istanbul
DOI :
10.1109/ICMSAO.2015.7152214
