Ravi Kumar Email & Phone Number

I have always been fascinated by machinery. Growing up, I spent countless hours tinkering with lathe machines in my home, inspired by my father’s work. My curiosity for how things work led me to pursue a Bachelor’s degree in Technology from the National Institute of Technology Jalandhar (NITJ), setting the foundation for my passion in mechanical engineering.Today, as a research scholar at the Indian Institute of Technology Kanpur, I am committed to developing cutting-edge technological solutions for the railway industry. My work focuses on vibration-based condition monitoring of railway components, particularly in LHB-type rolling stock. I specialize in flexible multibody dynamic modeling and fault detection, leveraging machine learning and deep learning techniques such as convolutional neural networks (CNN), recurrent neural networks (RNN), and transfer learning. I am also working to identify optimal sensor mounting locations inside railway coaches to capture high-quality signals for fault detection, ensuring the safety and reliability of railway operations.Key accomplishments in my career include:- Delivering a seminar on "Multi-body Dynamic Fault Simulation in Primary Suspension Systems and CNN-Based Diagnosis" at the 22nd Nordic Seminar on Railway Technology (NSRT 2024), KTH University, Stockholm, Sweden.- Presenting the paper "Suspension Failure Detection in Railway Bogie using Artificial Neural Network" at the 12th International Conference on Railway Bogies and Running Gears (BOGIE'22) in Budapest, Hungary.- Publishing an article titled "Primary Suspension Failure Analysis in FIAT Type LHB Bogies and Life Estimation" in the peer-reviewed journal *Engineering Failure Analysis* (Impact Factor: 3.73, 2022).- Presenting the paper "Condition Monitoring of Turning Tool Using Artificial Neural Network" at the National Conference of Condition Monitoring 2015.Specialties: Mechanical engineering, structural analysis, mechanical design, Finite Element Analysis (FEA), Numerical Analysis, fatigue analysis, railway design, flexible and multibody dynamics, vibration analysis, condition monitoring, deep learning, signal processing, LaTeX, Python scripting, Microsoft Office, Matlab, SolidWorks, and mathematical modeling.Currently, my research focuses on enhancing fault detection in rolling stock by exploring various deep learning algorithms, including CNNs, RNNs, transfer learning, data augmentation, and regularization techniques.I’m always eager to connect and collaborate with professionals across various industries—feel free to reach out!