Noureddine Bessous - University of El-Oued, Algeria
Salim Sbaa - University of Biskra, Algeria
Radouane Bousseksou - University of El-Oued, Algeria
Abdelkrim Allag - University of El-Oued, Algeria

This paper presents the mechanical fault detection in Squirrel Cage Induction Motors (SCIMs). In this study, we diagnosed the Rolling Bearing Element (RBE) faults in SCIM. Rolling bearing element faults is a major problem among different faults, which cause catastrophic damage to rotating machinery. thus, early detection of the RBE faults in SCIMs is a very important step. Among the key words of the mechanical vibrations for rotating electrical machines is the vibration analysis. This technique is generally called Motor Vibration Signature Analysis (MVSA) which is based on the vibration image. The vibration signal analysis is a one of the most important methods in the fault detection field. MVSA is generally based on Fast Fourier Transform (FFT) of the vibration signal. This research work has utilized the MVSA-FFT in detail, in order to detect the RBE faults. In addition, this study shows a new overlap between the characteristic frequencies of the RBE faults. In order to make an accurate analysis; it is important to know this overlap in advance. This new overlap has the advantage of expressing it by specific formulas which allows us to verify the additional harmonics carefully. The acquisition data is performed experimentally in order to ensure a wise decision.

induction motor, Fault Detection, Motor Vibration Signature Analysis, Outer Race Fault, Inner Race Fault, , Fast Fourier Transform, Vibration Signal Spectrum