PRADEEP KUNDU

About

My research focused on the broad domain of artificial intelligence for sustainable manufacturing, reliability engineering, predictive maintenance, digital twins, smart manufacturing and Industry 4.0. My research helps industries for reducing unplanned outages, increase productivity, automated quality control, and reduce operation and maintenance costs.

Employment

• KU Leuven Assistant Professor
  2023 - Present
• University of Cincinnati Post Doc Fellow
  2022 - 2023
• University of Strathclyde Research Associate
  2020 - 2022
• University of Alberta Overseas Visiting Doctoral Fellow
  2019 - 2019
• GE Power and Water Intern
  2014 - 2014

Education

• Indian Institute of Technology Indore Master of Technology
  2013 - 2015

Projects & Funding

Projects & funding information is unavailable.

Publications (42)

• Review of physics augmented: Physics-guided, physics-informed, and physics-encoded machine learning models in condition monitoring
  Engineering Applications of Artificial Intelligence 2026 DOI: 10.1016/j.engappai.2026.116100
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• A multi-channel data fusion-enabled multilevel graph-guided framework for fault diagnosis in rotating machinery under extreme biased data
  Information Fusion 2026 DOI: 10.1016/j.inffus.2026.104339
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• Modulated model network: A physics-informed machine learning method for high-precision signal reconstruction and enhanced fault diagnostics
  Engineering Applications of Artificial Intelligence 2026 DOI: 10.1016/j.engappai.2025.113514
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• A comprehensive review on generative artificial intelligence models for fault diagnostics and prognostics of rotating machineries
  Engineering Applications of Artificial Intelligence 2026 DOI: 10.1016/j.engappai.2025.113594
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• Electrochemical impedance spectroscopy and electric equivalent circuit modeling for open-cathode PEM fuel cell performance: A review
  Journal of Power Sources 2026 DOI: 10.1016/j.jpowsour.2026.239853
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• Review of condition monitoring approaches for ball screws
  Advanced Engineering Informatics 2026 DOI: 10.1016/j.aei.2025.104206
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• Development of data-driven, physics-based, and hybrid prognosis frameworks: a case study for gear remaining useful life prediction
  Journal of Intelligent Manufacturing 2025 DOI: 10.1007/s10845-024-02477-1
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• Detection of seeded fault and growing fault in rotating machinery under different speed scenarios using adaptive resonance-based signal sparse decomposition
  Structural Health Monitoring 2025 DOI: 10.1177/14759217241256130
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• A decision-level sensor fusion scheme integrating ultrasonic guided wave and vibration measurements for damage identification
  Mechanical Systems and Signal Processing 2024 DOI: 10.1016/j.ymssp.2024.111597
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• A novel technique for multiple failure modes classification based on deep forest algorithm
  Journal of Intelligent Manufacturing 2024 DOI: 10.1007/s10845-023-02185-2
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