Lester Cardoz
Papers
1
Total Citations
20
H-Index
1
About
Lester Cardoz is a researcher specializing in predictive maintenance and fault diagnostics for industrial machinery, with a particular focus on bearing fault detection. His most-cited work, "Bearing Fault Detection Using Comparative Analysis of Random Forest, ANN, and Autoencoder Methods" (2021, 20 citations), provides a critical benchmark for machine learning applications in rotating equipment health monitoring. Cardoz systematically evaluates three distinct approaches—Random Forest, Artificial Neural Networks, and Autoencoders—demonstrating how each method performs under varying fault conditions. This comparative analysis offers practical guidance for engineers selecting appropriate diagnostic tools, bridging the gap between theoretical machine learning and real-world industrial maintenance. His contributions are particularly valuable for advancing condition-based monitoring systems, helping to reduce unplanned downtime and extend machinery lifespan. By highlighting the strengths and limitations of each algorithm, Cardoz’s work supports the development of more robust, automated fault detection frameworks. His research continues to influence the growing field of intelligent maintenance, where data-driven methods are increasingly replacing traditional rule-based diagnostics.
Research Focus
Key Achievements
Top Papers
- 1