Cite this article
Machine Learning-Based Fault Detection in Three Phase Induction Motors Using Vibration Signals
DOI: https://doi.org/10.64180/oct.techai.260103
BibTeX
@article{vol4issue1machinelearningbasedfaultdetectioninthreephaseinductionmotorsusingvibrationsignals,
title = {Machine Learning-Based Fault Detection in Three Phase Induction Motors Using Vibration Signals },
author = {Muller, Abendroth},
journal = {AI Tech International Journal},
volume = {4},
number = {1},
pages = {20--25},
doi = {https://doi.org/10.64180/oct.techai.260103},
issn = {3079-4749}
}
RIS
TY - JOUR AU - Muller, Abendroth TI - Machine Learning-Based Fault Detection in Three Phase Induction Motors Using Vibration Signals JO - AI Tech International Journal VL - 4 IS - 1 SP - 20 EP - 25 DO - https://doi.org/10.64180/oct.techai.260103 AB - <p style="text-align:justify;">Three-phase induction motors are widely employed in industrial systems because of their robustness, reliability, relatively low maintenance requirements, and suitability for continuous operation. However, faults such as bearing defects, rotor imbalance, shaft misalignment, broken rotor bars, mechanical looseness, and stator related abnormalities can significantly reduce motor performance and may result in unexpected downtime and costly maintenance. Conventional fault-diagnosis approaches often depend on manual inspection, predefined thresholds, or specialized signal-processing techniques, which may have limitations when dealing with complex and changing operating conditions.</p> KW - Induction Learning KW - Analysis KW - Three-Phase Motor KW - Vibration Fault Machine Signal Detection KW - Predictive Maintenance. SN - 3079-4749 UR - https://techaijournal.com/article/vol-4-issue-1-machine-learning-based-fault-detection-in-three-phase-induction-motors-using-vibration-signals ER -