Assistant Professor , Sharif University of Technology, Best Researcher Award
Dr. S. Mohammadi is an accomplished mechanical engineer with a strong focus on vibration analysis, acoustics, and machine condition monitoring 🛠️🔍. He earned his Ph.D. from Amirkabir University of Technology, where he specialized in tire/road noise prediction and reduction 🔊🛣️. His research spans intelligent fault diagnosis, dynamic balancing, and advanced signal processing 📊🤖. With a deep commitment to industrial problem-solving and academic excellence, Dr. Mohammadi has published extensively in top-tier journals and conferences 🧠📚. His collaborative work with leading automotive and petrochemical industries demonstrates his practical impact in engineering innovation 🚗🏭.
Professional Profile
ORCID
Education and Experience
Dr. Mohammadi holds a Ph.D. (2016–2021), M.Sc. (2014–2016), and B.Sc. (2010–2014) in Mechanical Engineering from Amirkabir University of Technology 🎓🇮🇷. His doctoral research focused on modeling and predicting tire/road noise using semi-analytical and statistical methods 🔊📈. He has extensive experience in academia and industry, collaborating with IPCO and other companies on dynamic balancing, machine reliability, and condition monitoring ⚙️🏗️. He has published over 25 journal and conference papers and actively participates in technical events and applied engineering research, bridging theory and practice effectively 📚🛠️.
Professional Development
Dr. Mohammadi has significantly contributed to professional development in mechanical engineering through active involvement in research, publications, and conferences 🎤📄. He has attended numerous national and international events such as CMFD, ISAV, and IRNDT, presenting cutting-edge research on condition monitoring, acoustic diagnostics, and vibration analysis 🔍🧠. He continuously updates his skills in AI, machine learning, and signal processing for predictive maintenance and fault detection 🤖📊. His multidisciplinary approach enables practical solutions for complex industrial problems, making him a valuable contributor to academic and engineering communities 🌐🔧.
Research Focus
Dr. Mohammadi’s research centers on mechanical vibrations, acoustics, and intelligent fault detection using AI and signal processing 🧠🔊. His work addresses real-world engineering challenges like tire noise reduction, gearbox diagnostics, and turbine reliability ⚙️🏭. He combines statistical methods with machine learning to predict failures and optimize performance in rotating machinery, engines, and industrial systems 🤖🔧. His interdisciplinary expertise bridges mechanical design, acoustics, and data analytics to improve machinery health monitoring and performance efficiency 📉📈. His research supports sustainable and cost-effective engineering operations 🔄💡.
Awards and Honors
Dr. Mohammadi has received multiple recognitions for his research excellence and technical contributions 🎖️📚. He has been invited to present at prestigious conferences like CMFD, ISAV, and IRNDT and collaborated with top engineers and institutions on vibration and fault diagnosis projects 🤝🔍. His publications in high-impact journals such as Applied Acoustics, Journal of Vibration and Control, and Machines have earned critical acclaim from the academic community 🌟📰. He was also involved in award-supported industrial collaborations, including projects with IPCO and petrochemical companies, showcasing practical impact and innovation 🏭🏅.
Publication Top Notes
1.🔍 Intelligent Diagnosis of Rolling Element Bearings Under Various Operating Conditions Using an Enhanced Envelope Technique and Transfer Learning
📅 Published: April 2025 – Machines
📌 DOI: 10.3390/machines13050351
👥 Co-authors: Ali Davoodabadi, Mehdi Behzad, Hesam Addin Arghand, Len Gelman
🧠 Key Contribution: Developed an innovative technique combining advanced signal processing (enhanced envelope detection) with transfer learning, significantly improving fault diagnosis accuracy across variable operating conditions in rolling bearings. This paper bridges AI and mechanical reliability – a cutting-edge intersection in engineering diagnostics.
2.📊 A Comprehensive Study on Statistical Prediction and Reduction of Tire/Road Noise
📅 Published: October 2022 – Journal of Vibration and Control
📌 DOI: 10.1177/10775463211013184
👥 Co-authors: Abdolreza Ohadi, Mostafa Irannejad-Parizi
🧠 Key Contribution: Offers a data-driven, statistical framework for predicting and mitigating tire/road interaction noise, addressing environmental and comfort challenges in vehicle design. The study integrates modeling, statistical methods, and experimental validation, making it valuable for the automotive industry.
3.🔉 Effect of Modeling Sidewalls on Tire Vibration and Noise
📅 Published: September 2022 – Journal of Automobile Engineering (IMechE Part D)
📌 DOI: 10.1177/09544070211052368
👥 Co-author: Abdolreza Ohad
🧠 Key Contribution: Investigated how sidewall modeling precision influences vibrational behavior and noise in tires. The research advanced numerical tire modeling techniques, which are essential for designing quieter, more stable vehicles.
Conclusion
Dr. Mohammadi’s blend of deep theoretical knowledge, strong publication output, practical industrial applications, and multidisciplinary research makes him a standout researcher. His work addresses real-world engineering challenges with smart solutions, reinforcing his eligibility for the Best Researcher Award. He not only contributes to advancing scientific understanding but also to improving industrial reliability and performance — hallmarks of a truly impactful researcher 🏅🚀.