Prediction of bearing fault size by using model of adaptive neuro-fuzzy inference system
Yükleniyor...
Dosyalar
Tarih
2015
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IEEE 23rd Signal Processing and Communications Applications Conference, SIU
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
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 shaftbearing 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 0.1mm threshold value and it is observed that all the predicted values are inside this error band.
Açıklama
Anahtar Kelimeler
Diagnostics, Anfis, Bearings faults, Classification
Kaynak
WoS Q Değeri
Scopus Q Değeri
Cilt
Sayı
Künye
Kaplan, K., Kuncan, M., & Ertunc, H. M. (2015, May). Prediction of bearing fault size by using model of adaptive neuro-fuzzy inference system. In 2015 23nd Signal Processing and Communications Applications Conference (SIU) (pp. 1925-1928). IEEE.
