Detection of bearing defects in three-phase induction motors using Park’s transform and radial basis function neural networks | Zendy

İzzet Yilmaz Önel | Zendy; K. Burak Dalci | Zendy; İbrahim Şenol | Zendy

Open Access

Detection of bearing defects in three-phase induction motors using Park’s transform and radial basis function neural networks

Author(s) -

İzzet Yilmaz Önel,

K. Burak Dalci,

İbrahim Şenol

Publication year - 2006

Publication title -

sadhana

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.268

H-Index - 49

eISSN - 0973-7677

pISSN - 0256-2499

DOI - 10.1007/bf02703379

Subject(s) - induction motor , artificial neural network , bearing (navigation) , stator , matlab , computer science , radial basis function , basis (linear algebra) , signature (topology) , function (biology) , control engineering , engineering , artificial intelligence , control theory (sociology) , mathematics , electrical engineering , geometry , control (management) , voltage , operating system , evolutionary biology , biology

This paper investigates the application of induction motor stator current signature analysis (MCSA) using Park’s transform for the detection of rolling element bearing damages in three-phase induction motor. The paper first discusses bearing faults and Park’s transform, and then gives a brief overview of the radial basis function (RBF) neural networks algorithm. Finally, system information and the experimental results are presented. Data acquisition and Park’s transform algorithm are achieved by using LabVIEW and the neural network algorithm is achieved by using MATLAB programming language. Experimental results show that it is possible to detect bearing damage in induction motors using an ANN algorithm.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research