Vikash Kumar
KU Leuven, Case Western Reserve University, Muzaffarpur Institute of Technology
About
Vikash Kumar is currently working as a Research Associate in the Department of Mechanical Engineering at KU Leuven, Belgium. Previously, he served as a Research Associate at Case Western Reserve University, Cleveland, Ohio, US. He received his Ph.D. in Mechanical Engineering from the Indian Institute of Technology Patna, India in 2023. The thesis title is "Mathematical Modeling and Fault Diagnosis of the Motor-Gearbox System". His research interests include:
• Physics-based model
• Data-Driven model for machinery fault diagnosis
• Nonlinear analysis
• Signal processing
• Digital twin
• Multi-objective optimization
He is constantly working towards acquiring more knowledge to work on new aspects in his research area.
Employment
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KU Leuven Research Associate2024 - Present
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Case Western Reserve University Research Associate2023 - 2024
Education
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Muzaffarpur Institute of Technology B.Tech2011 - 2015
Projects & Funding
Projects & funding information is unavailable.
Publications (21)
- Gearbox fault diagnosis: A DL-based nonparametric filter approach Save
- ML-based bevel gearbox fault diagnosis: An extensive time domain feature extraction approach with limited data Save
- An integrated gear tooth crack analysis of coupled electromechanical model: A complexity-based approach Save
- Nonlinear dynamic analysis of spur gear pair for varying crack depth scenario Save
- Effect of Sampling Rate on Parametric and Non-parametric Data Preprocessing for Gearbox Fault Diagnosis Save
- Electromechanical Modelling and Multi Faults Diagnosis of Straight Bevel Gear Pair with Experimental Validation Save
- Volume Optimization of Two-Stage Helical Gear Train using Differential Evolution Algorithm Save
- Modified Lagrangian Formulation of Gear Tooth Crack Analysis using Combined Approach of Variable Mode Decomposition (VMD) and Time Synchronous Averaging (TSA) Save
- Gearbox fault diagnosis: A higher order moments approach Save
- A Comprehensive Assessment of Gearbox Tooth Faults Based on Dynamic Modelling and Machine Learning Save