Artificial immunity-based induction motor bearing fault diagnosis

Hakan ÇALIŞ; Abdülkadir ÇAKIR; Emre DANDIL

Makale detayları

Artificial immunity-based induction motor bearing fault diagnosis

Yazarlar:

Hakan ÇALIŞ | Abdülkadir ÇAKIR | Emre DANDIL

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2013 Vol.21 Issue 1, pp.1-25

Belge Tipi:

Makale

Yayın Tarihi:

12/2012

Belge Dili:

Türkçe

Document ID (Org No.):

2000743369

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632

Özet:

 

In this study, the artificial immunity of the negative  selection algorithm is used for bearing fault detection.  It is  implemented in MATLAB-based graphical user interface software.  The  developed software uses amplitudes of the vibration signal in the time  and frequency domains.  Outer, inner, and ball defects in the bearings  of the induction motor are detected by anomaly monitoring.  The time  instants of the fault occurrence and fault level are determined  according to the number of activated detectors.  Anomaly detection in  the frequency domain is implemented by monitoring the fault indicator  bearing frequencies and harmonics, calculated using the bearing  dimensions and number of rotor revolutions.  Due to the constant fault  location and closeness to the accelerometer, the outer race fault in  the bearing is the easiest fault type to determine.  However, the most  difficult fault type to detect is the ball defect.  By verification of  the detection results, the motor load has very little effect on the  fault.

 

In this study, the artificial immunity of the negative  selection algorithm is used for bearing fault detection.  It is  implemented in MATLAB-based graphical user interface software.  The  developed software uses amplitudes of the vibration signal in the time  and frequency domains.  Outer, inner, and ball defects in the bearings  of the induction motor are detected by anomaly monitoring.  The time  instants of the fault occurrence and fault level are determined  according to the number of activated detectors.  Anomaly detection in  the frequency domain is implemented by monitoring the fault indicator  bearing frequencies and harmonics, calculated using the bearing  dimensions and number of rotor revolutions.  Due to the constant fault  location and closeness to the accelerometer, the outer race fault in  the bearing is the easiest fault type to determine.  However, the most  difficult fault type to detect is the ball defect.  By verification of  the detection results, the motor load has very little effect on the  fault.

Makale detayları

Artificial immunity-based induction motor bearing fault diagnosis

Yazarlar: Hakan ÇALIŞ | Abdülkadir ÇAKIR | Emre DANDIL

Yayıncı: TUBITAK

Dergi: Turkish Journal of Electrical Engineering and Computer Science 2013 Vol.21 Issue 1, pp.1-25

Belge Tipi: Makale

Yayın Tarihi: 12/2012

Belge Dili: Türkçe

Document ID (Org No.): 2000743369

Sıklık: İki Aylık

eISSN: 1303-6203

pISSN: 1300-0632

Sayı

Yazarlar:

Hakan ÇALIŞ | Abdülkadir ÇAKIR | Emre DANDIL

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2013 Vol.21 Issue 1, pp.1-25

Belge Tipi:

Makale

Yayın Tarihi:

12/2012

Belge Dili:

Türkçe

Document ID (Org No.):

2000743369

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632