Abstract—Current trend toward industrial automation requires the replacement of supervision and monitoring roles traditionally performed by humans with artificial intelligent systems. This paper studies the use of hybrid intelligent system in Diagnosis of rotating machinery bearing defects. Vibration signals were collected for normal and various faulty conditions of the ball/roller bearing of the machinery. The acquired signal was processed with FFT and PSD in MATLAB to obtain the characteristic amplitudes from the frequency domain spectra of the signals. The obtained amplitude vector was used to train an adaptive neurofuzzy inference system (ANFIS) to classify and recognize normal and different faulty states. The system was tested, checked and validated with different sets of signal data. The validation data attests to the structural stability and performance of the system.

Index Terms—ANFIS, bearing fault, fault diagnosis, MATLAB, rotating machinery vibration

K. O. Oyedoja is with the Department of Technical Education, Emmanuel Alayande College of Education, Oyo, Oyo State, Nigeria (email:dojakay@ yahoo.com,)

Cite: Kayode Oyeniyi Oyedoja, "Diagnostics of Bearing Defects Using Vibration Signal," International Journal of Computer and Electrical Engineering vol. 4, no. 6, pp. 821-825, 2012.