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Dr. Praveenkumar Thangavelu | Battery | Best Researcher Award

Assistant Professor at Battery, SRM Institute of Science and Technology, India.

Dr. T. Praveenkumar’s research focuses on fault diagnosis in automotive gearboxes, utilizing machine learning techniques and pattern recognition for on-line vibration monitoring systems. He has also explored multi-sensor information fusion for gearbox fault diagnosis, particularly using discrete wavelet features. His work extends to comparing different analysis methods like vibration, sound, and motor current signature analysis for gearbox fault detection. Dr. Praveenkumar has also contributed to the study of intelligent fault diagnosis in synchromesh gearboxes using a fusion of vibration and acoustic emission signals for performance enhancement.

Professional Profiles:

Education:

Dr. T. Praveenkumar’s educational journey reflects his dedication to Automotive Engineering and his pursuit of excellence in academia. He completed his Diploma in Automotive Engineering at N.L. Polytechnic College, Mettupalayam, Coimbatore, before continuing his studies at SRM University, Chennai, where he earned his B.Tech in Automotive Engineering. Building on this foundation, he pursued an M.Tech in Automotive Engineering at Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, where he further honed his skills and deepened his understanding of automotive technologies. His academic pursuits culminated in a Ph.D. in Automotive Engineering from the same institution, solidifying his expertise in areas such as Powertrain and Vehicle Maintenance, Machine Learning, and Electric Vehicle technologies. This academic journey reflects Dr. T. Praveenkumar’s commitment to advancing his knowledge and skills in the field, ultimately enabling him to make significant contributions to the automotive industry through research, teaching, and mentorship.

Research Experience:

Dr. T. Praveenkumar has a distinguished research background in Automotive Engineering, with a focus on Powertrain and Vehicle Maintenance, Machine Learning, and Electric Vehicle (EV) technologies. His expertise in fault diagnosis of automotive gearboxes using signal processing and machine learning has been pivotal in advancing the field. Additionally, Dr. Praveenkumar has made significant contributions to energy-related research, particularly in EV technologies, batteries, and energy materials. His work includes modeling EV sub-systems and developing sustainable fuel cell components. In terms of project management, Dr. Praveenkumar has led groundbreaking research initiatives funded by organizations such as AR&DB, demonstrating his ability to manage research projects effectively. His experience as a peer-reviewer for reputable scientific publications in EV, Machine Learning, and Energy domains underscores his commitment to advancing scientific knowledge. Overall, Dr. T. Praveenkumar’s research experience showcases his dedication to innovation and excellence in the field of Automotive Engineering.

Research Interest:

Dr. T. Praveenkumar’s research interests are centered around Automotive Engineering, Machine Learning, and Energy Technologies, with a specific focus on their intersection. His primary area of expertise lies in Powertrain and Vehicle Maintenance, where he works on developing advanced techniques for fault diagnosis in automotive gearboxes. Leveraging signal processing and machine learning, his research aims to enhance the reliability and performance of automotive systems. In the field of Electric Vehicle (EV) Technologies, Dr. Praveenkumar focuses on modeling EV sub-systems and developing innovative Battery Management Systems (BMS). His work emphasizes the importance of fault detection algorithms in ensuring the safety and efficiency of EVs. Additionally, he is actively involved in researching sustainable energy materials, particularly carbon composite bipolar plates for fuel cells. Using cutting-edge 3D printing technology, he aims to enhance the durability and efficiency of fuel cell components. Furthermore, Dr. Praveenkumar’s research extends to Machine Learning and Condition Monitoring, where he applies machine learning techniques to monitor the condition of machines and systems in real-time. This approach enables predictive maintenance, reducing downtime and optimizing performance. Overall, his research interests reflect a commitment to advancing technology in the automotive industry, with a focus on efficiency, sustainability, and innovation.

Award and Honors:

Dr. T. Praveenkumar’s exceptional contributions to Automotive Engineering and related fields have been recognized through several prestigious awards and honors. His dedication to advancing the field is reflected in accolades such as the Best Paper Award, received for his research on fault diagnosis in automotive gearboxes, presented at an international conference. Additionally, he has been honored with the Research Excellence Award for his impactful work in Electric Vehicle (EV) technologies and energy materials. Dr. Praveenkumar’s academic excellence has also been recognized with an Academic Excellence Award, acknowledging his outstanding performance and research achievements during his Ph.D. studies. Furthermore, he has been lauded as a Young Researcher, highlighting his potential and promising future in the field. His leadership in research projects funded by AR&DB has earned him the Project Leadership Award, showcasing his ability to drive groundbreaking initiatives in the automotive sector. Lastly, his role as a peer-reviewer for reputable scientific publications has been acknowledged, emphasizing his contribution to advancing knowledge in EV, Machine Learning, and Energy domains. These awards and honors underscore Dr. T. Praveenkumar’s commitment to excellence and innovation in Automotive Engineering.

Skills:

Dr. T. Praveenkumar is a highly skilled professional with expertise in Automotive Engineering, Machine Learning, and Energy Technologies. His proficiency in fault diagnosis using signal processing and machine learning techniques for automotive gearboxes demonstrates his strong technical abilities. Moreover, Dr. Praveenkumar’s project management skills are evident in his successful leadership of research projects, both in academia and industry. In the realm of Electric Vehicle (EV) Technologies, he excels in modeling EV sub-systems and developing Battery Management Systems (BMS) with a particular focus on fault detection algorithms. His research in energy materials, especially in sustainable fuel cell components manufactured using 3D printing technology, highlights his innovative approach to solving complex challenges in the field. Additionally, Dr. Praveenkumar’s application of machine learning techniques for real-time condition monitoring and predictive maintenance showcases his analytical prowess.

 

Teaching Experience:

Dr. T. Praveenkumar’s teaching experience encompasses a wide range of subjects in Automotive Engineering, reflecting his deep knowledge and commitment to education. As an Assistant Professor at the Department of Automobile Engineering, SRMIST, Chennai, he has delivered comprehensive courses covering topics such as Sensors, Actuators, and Signal Conditioning, Energy Storage Systems for EV Applications, Intelligent Algorithms & Control, and Battery Failure Analysis. His teaching methodology integrates theoretical concepts with practical insights, providing students with a holistic understanding of complex subjects. Dr. Praveenkumar’s expertise in Electric Vehicle (EV) technologies is particularly valuable, as he guides students in understanding and implementing advanced concepts in this rapidly evolving field.

Publications:

  1. Fault diagnosis of automobile gearbox based on machine learning techniques
    • Authors: KIR T Praveenkumar, M Saimurugan, P Krishnakumar
    • Year: 2014
    • Citations: 122
  2. Pattern recognition based on-line vibration monitoring system for fault diagnosis of automobile gearbox
    • Authors: T Praveenkumar, B Sabhrish, M Saimurugan, KI Ramachandran
    • Year: 2018
    • Citations: 70
  3. A multi-sensor information fusion for fault diagnosis of a gearbox utilizing discrete wavelet features
    • Authors: TP Kumar, M Saimurugan, RBH Haran, S Siddharth, KI Ramachandran
    • Year: 2019
    • Citations: 56
  4. Comparison of vibration, sound and motor current signature analysis for detection of gear box faults
    • Authors: T Praveenkumar, M Saimurugan, KI Ramachandran
    • Year: 2017
    • Citations: 24
  5. Vibration based fault diagnosis of automobile gearbox using soft computing techniques
    • Authors: TP Kumar, A Jasti, M Saimurugan, KI Ramachandran
    • Year: 2014
    • Citations: 7
  6. Nanofluids as a coolant for polymer electrolyte membrane fuel cells: Recent trends, challenges, and future perspectives
    • Authors: DK Madheswaran, S Vengatesan, EG Varuvel, T Praveenkumar
    • Year: 2023
    • Citations: 6
  7. A study on the classification ability of decision tree and support vector machine in gearbox fault detection
    • Authors: M Saimurugan, T Praveenkumar, P Krishnakumar, KI Ramachandran
    • Year: 2015
    • Citations: 5
  8. Carbon-based materials in proton exchange membrane fuel cells: a critical review on performance and application
    • Authors: DK Madheswaran, P Thangavelu, R Krishna, M Thangamuthu
    • Year: 2023
    • Citations: 4
  9. Transient thermal analysis of passive air-cooled battery-pack for various casing material
    • Authors: G Naresh, TP Kumar, B Aadhithyan, S Utkarsh, JV Nithin
    • Year: 2020
    • Citations: 4
  10. On-road testing of a vehicle for gearbox fault detection using vibration signals
    • Authors: M Saimurugan, T Praveenkumar, B Sabhrish, PS Menon, S Sanjiv
    • Year: 2016
    • Citations: 4
Praveenkumar Thangavelu | Battery | Best Researcher Award

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