Deep Learning and Few‐Shot Learning–Enabled Machine Health Monitoring for Rotating Machinery: A Critical Review of Vibration‐Based Fault Diagnosis, Prognostics, and Industrial Deployment

ABSTRACT The integration of intelligent machine health monitoring systems (MHMS) into rotating machinery and industrial equipment has accelerated due to the growing demand for productivity, dependability, and predictive maintenance in contemporary production systems. Deep learning (DL), one of the new methods, has proven to be quite effective at directly extracting discriminative features from high‐dimensional vibration and acoustic signals, doing away with the requirement for human feature engineering. The DL approaches used for vibration‐based defect diagnosis and prognostics of rotating machinery are critically and methodically reviewed in this research. Signal properties, fault modes, and experimental settings are thoroughly investigated in representative public benchmark datasets, such as those from Case Western Reserve University, Paderborn University, IMS, and Pronostia. Major DL architectures, convolutional neural networks (CNN), autoencoders (AE), deep belief networks (DBN), recurrent neural networks (RNN/LSTM), and hybrid CNN–LSTM frameworks are compared in terms of feature learning mechanisms, robustness to noise, computational complexity, and suitability for real‐time industrial deployment. In addition, the paper also looks at recent developments in few‐shot and meta‐learning techniques that deal with limited fault samples and data imbalance in real‐world mechanical systems. From the standpoint of mechanical engineering, the difficulties of domain shift, interpretability, computational limitations, and edge implementation are severely examined. Lastly, a real‐time deployment‐oriented Few‐Shot based MS‐1D‐CNN framework deployed in an industrial environment with a focus on scalability, robustness, and integration with industrial control systems is described for real‐time intelligent MHMS in Industry 4.0 scenarios. Consolidated insights and future research prospects for creating dependable, flexible, and computationally effective DL solutions for rotating machinery health management are provided by this review.