A Review – BearMath: Bearing Fault Diagnostics Using Machine Learning

Condition monitoring, predictive maintenance, and intelligent fault diagnosis are important for the reliability of rotating machinery and industrial systems. Traditional fault detection methods have been greatly enriched by the recent development of deep learning and advanced signal processing techniques, which harness powerful reactionists, such as CNNs, recurrent architectures, and transfer learning for fail-safe and adaptive fault identification. This review provides a systematic survey of this transition from classical machine condition monitoring approaches (like wavelet transforms and spectral analysis) to modern data-driven deep learning schemes. Drawing on an extensive array of methodologies, such as convolutional and generative adversarial networks (GANs), domain adaptation, and hybrid models that combine deep learning with time frequency representations for enhanced accuracy and generalization, we do a deep dive into the various methods of approach. Emphasis is placed on bearing fault detection, a crucial theme of rotating machinery health monitoring, encompassing a review of the Case Western Reserve University (CWRU) bearing dataset and further benchmark datasets for training and validation. Lastly, we present the challenges/gaps and future research directions, calling for the need for more generalized, interpretable, real-world applicable fault diagnosis models.