Anuj Gajula
Papers
1
Total Citations
20
H-Index
1
About
Anuj Gajula is a researcher whose work sits at the intersection of machine learning and mechanical systems, with a particular focus on predictive maintenance and fault diagnostics. His most-cited paper, "Bearing Fault Detection Using Comparative Analysis of Random Forest, ANN, and Autoencoder Methods" (2021), has garnered 20 citations, establishing him as a contributor to the growing field of intelligent condition monitoring. In this study, Gajula systematically compared three distinct machine learning approaches—Random Forest, Artificial Neural Networks, and Autoencoders—for detecting faults in rotating machinery bearings, a critical component in industrial equipment. His comparative analysis provided valuable insights into the trade-offs between model complexity, accuracy, and interpretability, offering practical guidance for engineers seeking to implement automated fault detection systems. By demonstrating the strengths of simpler ensemble methods alongside deep learning architectures, Gajula’s work helps bridge the gap between academic research and real-world industrial applications. His contributions are particularly relevant as industries increasingly adopt data-driven strategies to reduce downtime and maintenance costs. Gajula’s research continues to inform best practices in applying machine learning to mechanical fault diagnosis.
Research Focus
Key Achievements
Top Papers
- 1