Induction motors are widely used at industrial level and many researches have been conducted to predict faults and ensure their continuous operation. The present study shows a fault detection system based on the Motor Current Signature Analysis (MCSA) technique which is a non-invasive method, and the Fast Fourier Transform (FFT) algorithm to perform spectral analysis on the current to detect specific components that characterize faults in different conditions such as damaged bearings, shorted winding in turns, and broken bars. The tests were conducted using a motor test bench and the results show a spectral value obtained by FFT in the frequency of 300 Hz for faults in bearings of the motors 1 and 2. Results also reveal coincidence of 96.9% and 99.46% compared to the results obtained through the theoretical equations included in the MCSA technique, representing values that are within the fault frequency bands on 1x and 2x respectively.

Diagnosis of Incipient Faults in Induction Motors Using MCSA and Thermal Analysis

Most industrial processes require reliability, safe and continuous operation of electric motors. However, unexpected failures result in production losses, high emergency maintenance costs, damage to other related machines and prolonged shutdowns. Therefore, the development of failure analysis, detection and prevention systems is of high importance for the industrial sector. The following paper proposes the generation of a model of a squirrel cage induction motor in the software tool Simulink capable of simulating: (i) broken bars and broken rotor rings and (ii) failures in the bearings of the motor, based on the Motor Current Signature Analysis (MCSA) technique which is a non-invasive method, and the fast Fourier transform (FFT) algorithm to perform spectral analysis on the current to detect specific components that characterize faults in these conditions.

Assessment of Engineering Techniques for Failures Simulation in Induction Motors Using Numerical Tool

Comparative Analysis of High Frequencies for the Broken Bar Fault Diagnosis Using MCSA and Park’s Vector Demodulation

The paper is focused on one of the important technique practised under Condition Based Maintenance (CBM) procedure, which is known as Motor Current Signature Analysis (MCSA) The technique proposes the study of side-band frequencies, which are generated due to fault conditions within the machine These side-band frequencies are later analyzed using Fast Fourier Transforms (FFT) and Discrete Wavelet Transforms (DWT) In the present work, the two different fault conditions namely Broken Rotor Bar (BRB) and Stator Winding Fault (SWF) are jointly analyzed, as a novel approach The results have been compared with the fault diagnosis of healthy machine This contribution can be helpful in further carrying out study on prognostics of the machines, and in reducing the unscheduled breakdown while increasing the reliability of the machine

A C Abhinandan

Papers

Fault diagnosis of an induction motor through motor current signature analysis, FFT & DWT analysis