Review on deep learning classifiers for faults diagnosis of rotating industrial machinery

Rotating industrial machinery underpins manufacturing and infrastructure, yet robust fault diagnosis remains crucial for optimizing efficiency and minimizing downtime. This review comprehensively analyzes the transformative impact of Deep Learning (DL) on fault diagnosis in this critical domain. Drawing insights from eight thematic tables, we unveil key findings, explore future implications, and offer practical recommendations. Our review highlights the significant advantages of DL over traditional methods, showcasing its superior accuracy and versatility across diverse applications. We emphasize the paramount role of data quality, diversity, and representativeness in building robust models, identifying specific data types influencing optimal DL architectures. By delving into the strengths and limitations of common techniques, we provide a valuable roadmap for practitioners to adapt models for specific needs. Finally, we call for collaborative efforts between researchers and practitioners to accelerate the development and application of these game-changing solutions. This comprehensive review promises for further advancements in DL-based fault diagnosis, fostering a future where industrial machinery operates with unprecedented reliability and efficiency.