Signature Analysis as a Medium for Detecting Faults in Induction Motor

Abstract

An induction motor (IM) is an essential component in many industries and power plants. Therefore, for most applications requiring IMs, the reliability, efficiency and performance are of great importance. Also, since the costs of break down and unforeseen shut downs are extremely high and the need for high reliability is extensive, condition monitoring of IM became increasing significantly. There are several condition monitoring techniques, e.q. vibration and thermal monitoring. However, those monitoring techniques require sensors, which might be expensive. On the other hand, electrical monitoring such as Motor Current Signature Analysis (MCSA) does not require the use of extra sensors. The MCSA technique makes use of the stator current spectrum for detecting fault frequencies. When there is a fault in the motor, the frequency of the line current becomes different than that of a healthy motor. So, in this work, unbalance and misalignment fault detection using MCSA in LabVIEW with the help of FFT and ANN will be presented