Arshan Irani
Papers
1
Total Citations
20
H-Index
1
About
Arshan Irani is a researcher specializing in machine learning applications for predictive maintenance and fault detection in industrial systems. His work focuses on developing and comparing advanced computational methods—including Random Forest, Artificial Neural Networks (ANN), and Autoencoder architectures—to improve the reliability of bearing fault detection in rotating machinery. Irani’s most-cited paper, "Bearing Fault Detection Using Comparative Analysis of Random Forest, ANN, and Autoencoder Methods" (2021), has garnered 20 citations, establishing a foundation for data-driven diagnostics in manufacturing and energy sectors. By systematically evaluating these algorithms, he has contributed to more accurate, cost-effective condition monitoring, reducing downtime and maintenance costs. His research bridges the gap between traditional signal processing and modern deep learning, offering practical insights for engineers and researchers. Irani’s work is particularly notable for its emphasis on comparative analysis, providing a clear benchmark for selecting optimal models in real-world fault detection scenarios. His contributions continue to influence the growing field of intelligent industrial maintenance, making him a valuable voice for students and professionals exploring machine learning in engineering applications.
Research Focus
Key Achievements
Top Papers
- 1