PREDICTION OF BEARING FAULT SIZE BY USING MODEL OF ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM

被引:0
作者
Kaplan, Kaplan [1 ]
Kuncan, Melih [1 ]
Ertunc, H. Metin [1 ]
机构
[1] Kocaeli Univ, Mekatron Muhendisligi Bolumu, Izmit, Turkey
来源
2015 23RD SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU) | 2015年
关键词
diagnostics; ANFIS; bearings faults; classification;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Condition monitoring of bearings faults which have vital importance in machines and detection of faults earlier have very big importance in terms of disruption of process. In this study, certain sizes artificial faults are generated by the laser beam on inner rings of bearing and vibration signals are obtained from these bearings in a shaft-bearing setup. It is aimed to diagnose the size of the defects occurring in the bearings by using adaptive neuro-fuzzy inference system (ANFIS) model in the study. After extracting the real-time features of obtained vibration data, they are multiplied by the specific weight and they are given as input to the generated classification model. It has been observed difference of features extracted from of 0.15 cm, 0.5 cm, 0.9 cm diameter inner ring faulty bearings created by the laser depending on size of faults. ANFIS classification model is developed by using these features and the size of the faults occurring in these bearings were calculated with an actual error 2.40 %. Then a error band are created with 0.1 mm threshold value and it is observed that all the predicted values are inside this error band.
引用
收藏
页码:1925 / 1928
页数:4
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