International Journal of Science, Engineering and TechnologyAuthors: Gaurav Suresh Ambatkar, Swapnil Choudhary
Abstract: Bearings are among the most failure-prone components in induction motors, and their degradation often leads to unplanned downtimes and considerable financial losses. Early and accurate fault diagnosis of motor bearings is thus a critical concern in industrial practice. While classical approaches such as vibration analysis and motor current signature analysis (MCSA) have long served this purpose, the emergence of convolutional neural networks (CNNs) has transformed the diagnosis process through automated feature extraction and superior multi-class discrimination. This paper presents a detailed and comprehensive review of recent advances in CNN-based bearing fault diagnosis for induction motors. Detailed types of bearing faults are discussed, alongside a critical comparison of traditional and CNN-based methodologies, the operational flow of CNNs, specific model architectures, and the standing of CNNs in relation to other leading diagnostic approaches
DOI: http://doi.org/10.5281/zenodo.20233165
