Broken Rotor Bar Fault Detection in Induction Motors Using Motor Current Signature Analysis (MCSA)

Induction motors are commonly used in various industrial applications due to their reliability and robustness. However, they are susceptible to faults that can compromise their performance and efficiency. One of the common faults encountered in induction motors is Broken Rotor Bars, which can lead to rotor imbalance, increase vibration, and reduce the efficiency of the motor. Detecting and diagnosing this fault is critical to ensure the proper operation of the motors for industrial purposes and prevent costly downtime. This paper investigates the comparative current signature analysis of Broken Rotor Bar (BRB) fault detection in induction motors using different windowing functions. The study explores the effectiveness of various windowing functions including Hanning, Hamming, Blackman, and Flattop, in enhancing the analysis of stator current signals for fault detection purposes. The analysis is conducted using Fast Fourier Transform (FFT) techniques. The findings provide insights into the impact of windowing functions on fault detection performance and suitability for motor maintenance applications, specifically in detecting faults such as broken rotor bar faults in induction motors.