ABSTRACT:
Stator current analysis has become a well-established approach for fault detection in induction motors, employed in both research and industrial applications. This project presents the results of a hardware-based study using current signal analysis to detect bearing faults in a three-phase induction motor at 60% and 70% of full load. We propose a novel diagnostic method that combines established techniques such as Fast Fourier Transform (FFT) and Short-Time Fourier Transform (STFT) with the less commonly used Impulse Invariant Response (IIR) technique. Current signal data is acquired using ACS712 current sensor, a PIC microcontroller and processed in MATLAB to extract dominant frequency components. These are then compared to those of a healthy motor to identify and analyze potential faults. Additionally, the study incorporates statistical measures such as mean, variance, and kurtosis for a comprehensive analysis. Our findings, based on tests at different load levels, demonstrate the effectiveness of these techniques in early detection of bearing faults. Future work aims to refine these methods and explore their application in diverse real-time industrial settings, potentially revolutionizing maintenance approaches by preventing machinery breakdowns and optimizing operational efficiency.
KEYWORDS: Three Phase Induction motors, Fast Fourier transform, Infinite Impulse Response, Short�time Fourier transform, , Fault Detection, Bearing fault
Website Link: https://motorfaultanalyser.netlify.app/
APP LINK: https://drive.google.com/file/d/1rHF7O7k_O-Nkvys4gasZxh4hvMTtbNbw/view?usp=drive_link
Guidelines for using the app:
- Download the MATLAB application from the provided drive link.
- Collect Healthy and Input motor current signals in CSV file format.
- Ensure that all these files are present in same directory.