Feature Extraction of Ball Bearings in Time-Space and...

Feature Extraction of Ball Bearings in

Time-Space and Estimation of Fault Size

with Method of ANN Kaplan Kaplan1, Samet Bayram

2, Melih Kuncan

3, H.Metin Ertunç

4

kaplan.kaplan@kocaeli.edu.tr

sametbayram21@hotmail.com.tr

melih.kuncan@kocaeli.edu.tr

hmertunc@kocaeli.edu.tr

Abstract Faults in bearings used in machines cause downtime

and leads to catastrophic results on the machining operations. In

this study, specific sizes of the artificial bearings defects are

created and vibration signals were obtained from a shaft-bearing

system. The purpose of this study is to diagnose the size of the

defects occurring in bearings by using Artificial Neural

Networks(ANN) model. Features of vibration data are extracted

in real time and are multiplied with specific weights; then they

were given as input to the ANN model. Statistical properties of

bearings faults are observed that their values vary depending on

fault dimensions in real-time. These features are examined by

using ANN and the size of the defects occurring in bearings are

classified with 100% success, on the other hand the prediction

permonfance of actual error for a ANN model is found 2%.

Keywords- Artificial neural networks; bearings; diagnosing

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A. The Test Platform

B. Raw Vibration Data and Feature Extraction

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C. Establishment of Artificial Neural Network Models

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D. Simulation

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