Dr Anurag Choudhary

About

Dr. Anurag Choudhary (Member, IEEE) received a B.E. degree in Electrical and Electronics Engineering from Uttarakhand Technical University, Dehradun, India, in 2015, and an M.E. degree in instrumentation and control from Panjab University, Chandigarh, India in 2018. Dr. Choudhary completed his Ph.D. in 2024 from the Indian Institute of Technology (IIT) in Delhi, India. His areas of specialization are Electric vehicle Diagnostics including condition monitoring, machine fault diagnosis, vibration monitoring, infrared thermography, and machine learning. He has 3 years of experience, 1 year teaching, and 2 years of research and development. He is a student member of IEEE (USA) and the International Engineers' Association, Hong Kong. He has more than 15 Research Articles to his credit. His areas of specialization are Condition Monitoring, Machine Fault Diagnosis, Vibration Monitoring, Infrared Thermography, and Machine Learning.

Employment

• University of Hong Kong Post-doctoral Fellow
  2024 - Present
• Indian Institute of Technology Delhi Earrly doc fellow
  2024 - 2024
• Central Scientific Instruments Organisation CSIR Project Assistant _III
  2018 - 2019
• College of Engineering Roorkee Lecturer
  2015 - 2016

Education

• Indian Institute of Technology Delhi Doctor of Philosophy
  2020 - 2024

Projects & Funding

Projects & funding information is unavailable.

Publications (34)

• Review of recent trends of advancements in multilevel inverter topologies with reduced power switches and control techniques
  IET Power Electronics 2026 12 citations DOI: 10.1049/pel2.12564
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• Viability and Impact of Electric Vehicle Transition in India's Focus on Delhi: A Comprehensive Review
  Transactions of the Indian National Academy of Engineering 2025 3 citations DOI: 10.1007/s41403-025-00524-8
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• Multimodal Fusion-Based Fault Diagnosis of Electric Vehicle Motor for Sustainable Transportation
  IEEE Transactions on Transportation Electrification 2025 18 citations DOI: 10.1109/TTE.2024.3502466
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• Nature‐inspired artificial bee colony‐based hyperparameter optimization of CNN for anomaly detection in induction motor
  Expert Systems 2024 8 citations DOI: 10.1111/exsy.13407
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• A generalized method for diagnosing multi-faults in rotating machines using imbalance datasets of different sensor modalities
  Engineering Applications of Artificial Intelligence 2024 44 citations DOI: 10.1016/j.engappai.2024.107973
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• State-of-the-Art Technologies in Fault Diagnosis of Electric Vehicles: A Component-Based Review
  IEEE Transactions on Transportation Electrification 2023 91 citations DOI: 10.1109/TTE.2022.3209166
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• Multi-input CNN based vibro-acoustic fusion for accurate fault diagnosis of induction motor
  Engineering Applications of Artificial Intelligence 2023 176 citations DOI: 10.1016/j.engappai.2023.105872
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• Mel-spectrogram based Approach for Fault Detection in Ball Bearing using Convolutional Neural Network
  2023 2 citations DOI: 10.1145/3592307.3592352
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• Multi Sensor based Bearing Fault Diagnosis of Switched Reluctance Motor for Electric Vehicle
  2023 2 citations DOI: 10.1145/3592307.3592348
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• Fault Diagnosis of Electric Two-Wheeler Under Pragmatic Operating Conditions Using Wavelet Synchrosqueezing Transform and CNN
  IEEE Sensors Journal 2023 31 citations DOI: 10.1109/JSEN.2023.3239383
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