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@article{205324,
        author = {Ishan Thakar and Keval Bhavsar and Umang Parmar and Pina Bhatt},
        title = {A Comprehensive Review of Bearing Fault Identification Techniques Using the CWRU Dataset},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {6430-6441},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=205324},
        abstract = {Rolling element bearing faults are one of the major issues that occur in any industrial machine. Hence, predicting fault identification accurately is not only essential for the reliable operation but also for its predictive maintenance. The Case Western Reserve University (CWRU) bearing dataset is one of the most widely used benchmarking datasets for developing and evaluating bearing fault diagnosis techniques. This paper presents a comprehensive overview of bearing fault identification methods based on the CWRU dataset, covering signal processing, machine learning, and deep learning approaches. Traditional techniques such as time-domain, frequency-domain, and time-frequency domain analyses are examined alongside machine learning algorithms like Support Vector Machines, Decision Trees, and Random Forests. Recent advances in deep learning, particularly Convolutional Neural Networks and Long Short-Term Memory networks, are also discussed. The review highlights the strengths and limitations of existing methods, identifies key research gaps related to model generalisation and its real-world deployment, and outlines future directions for intelligent and robust bearing condition monitoring systems.},
        keywords = {Bearing Fault Diagnosis, CWRU Dataset, Condition Monitoring, Deep Learning, Machine Learning, Predictive Maintenance, Rotating Machinery, Vibration Analysis.},
        month = {June},
        }